At opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as opinion-as-a-service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
In recent years, many daily processes such as internet web searching, e-mail filter-ing, social media services, e-commerce have benefited from machine learning tech-niques (ML). The implementation of ML techniques has been largely focused on blackbox methods where the general conclusions are not easily interpretable. Hence, theelaboration with other declarative software models to identify the correctness and com-pleteness of the models is not easy to perform. On the other hand, the emerge of somelogic-based machine learning techniques with their advantage of white box approachhave been proven to be well-suited for many software engineering tasks. In this paper,we propose the use of a logic-based approach to learn user preference in the form ofpairwise comparisons. APARELL as a novel approach of inductive learning is able tomodel the user’s preferences in description logic representation. This offers a rich, re-lational representation which is then can be used to produce a set of recommendations.A user study has been performed in our experiment to evaluate the implementation ofpairwise preference recommender system when compared to a standard list interface.The result of the experiment shows that the pairwise interface was significantly betterthan the other interface in many ways.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
opinion feature extraction using enhanced opinion mining technique and intrin...INFOGAIN PUBLICATION
Mining patterns are the main source of opinion feature extraction techniques, which was individually evaluated corpus mostly belong to evaluated corpus. A measure called Domain Relevance is used to identify candidate features from domain dependent and domain independent corpora both. Opinion Features originated are relevant to a domain. For every extracted candidate feature its individual Intrinsic Domain Relevance and Extrinsic Domain Relevance values are registered. Threshold has been compared with these values and recognizes as best candidate features. In this thesis, By applying feature filter creation the features from online reviews can be identified .
FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSISmlaij
Sentiment analysis and Opinion mining has emerged as a popular and efficient technique for information retrieval and web data analysis. The exponential growth of the user generated content has opened new horizons for research in the field of sentiment analysis. This paper proposes a model for sentiment analysis of movie reviews using a combination of natural language processing and machine learning approaches. Firstly, different data pre-processing schemes are applied on the dataset. Secondly, the behaviour of twoclassifiers, Naive Bayes and SVM, is investigated in combination with different feature selection schemes to
obtain the results for sentiment analysis. Thirdly, the proposed model for sentiment analysis is extended to
obtain the results for higher order n-grams.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Word2Vec model for sentiment analysis of product reviews in Indonesian languageIJECEIAES
Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve BayesTELKOMNIKA JOURNAL
Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Investigating the Effects of Personality on Second Language Learning through ...CSCJournals
The aim of this research is to determine Second Language Acquisition and personality variable from affective factors analyzed by Artificial Neural Network in freshman class of both university students. This study presents an intelligent approach to the investigation of positive effects of personality on second language learning. For this purpose, watching TV, reading books, magazines, newspaper, listening to the radio, talking to a native English friend, and talking to people at school are investigated. The tool of our research is a survey (questionnaire) to collect a data in order to quantify students ‘personality traits based on affective factors. The questionnaire consists of two parts. The first part consists of Yes/ No questions while the second part uses a 4 point Likert scale with 5 items that indicates what helped students personally to learn English. The participants were 160 students from two private universities in Bosnia and Herzegovina, International Burch University (90 students) and International University of Sarajevo (70). The subjects’ major was English. The first part of the survey was analyzed using ANN, and the second part using statistical analysis. Both data analysis were processed by transferring answers to an Excel sheet. For each measure, mode, standard deviation, median were calculated to determine students’ personality factors. We used two different types of analysis in order to show that different kinds of analysis can be done.
Document Retrieval System, a Case StudyIJERA Editor
In this work we have proposed a method for automatic indexing and retrieval. This method will provide as a
result the most likelihood document which is related to the input query. The technique used in this project is
known as singular-value decomposition, in this method a large term by document matrix is analyzed and
decomposed into 100 factors. Documents are represented by 100 item vector of factor weights. On the other
hand queries are represented as pseudo-document vectors, which are formed from weighed combinations of
terms.
Data Collection Methods for Building a Free Response Training SimulationMelissa Moody
Master of Science in Data Science capstone project researchers Vaibhav Sharma, Beni Shpringer, and Michael Yang, along with UVA School of Engineering M.S. student Martin Bolger and Ph.D. students Sodiq Adewole and Erfaneh Gharavi, sought to develop new methods for collecting, generating, and labeling data to aid in the creation of educational, free-input dialogue simulations.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
A simplified classification computational model of opinion mining using deep ...IJECEIAES
Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
opinion feature extraction using enhanced opinion mining technique and intrin...INFOGAIN PUBLICATION
Mining patterns are the main source of opinion feature extraction techniques, which was individually evaluated corpus mostly belong to evaluated corpus. A measure called Domain Relevance is used to identify candidate features from domain dependent and domain independent corpora both. Opinion Features originated are relevant to a domain. For every extracted candidate feature its individual Intrinsic Domain Relevance and Extrinsic Domain Relevance values are registered. Threshold has been compared with these values and recognizes as best candidate features. In this thesis, By applying feature filter creation the features from online reviews can be identified .
FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSISmlaij
Sentiment analysis and Opinion mining has emerged as a popular and efficient technique for information retrieval and web data analysis. The exponential growth of the user generated content has opened new horizons for research in the field of sentiment analysis. This paper proposes a model for sentiment analysis of movie reviews using a combination of natural language processing and machine learning approaches. Firstly, different data pre-processing schemes are applied on the dataset. Secondly, the behaviour of twoclassifiers, Naive Bayes and SVM, is investigated in combination with different feature selection schemes to
obtain the results for sentiment analysis. Thirdly, the proposed model for sentiment analysis is extended to
obtain the results for higher order n-grams.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Word2Vec model for sentiment analysis of product reviews in Indonesian languageIJECEIAES
Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.
Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve BayesTELKOMNIKA JOURNAL
Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Investigating the Effects of Personality on Second Language Learning through ...CSCJournals
The aim of this research is to determine Second Language Acquisition and personality variable from affective factors analyzed by Artificial Neural Network in freshman class of both university students. This study presents an intelligent approach to the investigation of positive effects of personality on second language learning. For this purpose, watching TV, reading books, magazines, newspaper, listening to the radio, talking to a native English friend, and talking to people at school are investigated. The tool of our research is a survey (questionnaire) to collect a data in order to quantify students ‘personality traits based on affective factors. The questionnaire consists of two parts. The first part consists of Yes/ No questions while the second part uses a 4 point Likert scale with 5 items that indicates what helped students personally to learn English. The participants were 160 students from two private universities in Bosnia and Herzegovina, International Burch University (90 students) and International University of Sarajevo (70). The subjects’ major was English. The first part of the survey was analyzed using ANN, and the second part using statistical analysis. Both data analysis were processed by transferring answers to an Excel sheet. For each measure, mode, standard deviation, median were calculated to determine students’ personality factors. We used two different types of analysis in order to show that different kinds of analysis can be done.
Document Retrieval System, a Case StudyIJERA Editor
In this work we have proposed a method for automatic indexing and retrieval. This method will provide as a
result the most likelihood document which is related to the input query. The technique used in this project is
known as singular-value decomposition, in this method a large term by document matrix is analyzed and
decomposed into 100 factors. Documents are represented by 100 item vector of factor weights. On the other
hand queries are represented as pseudo-document vectors, which are formed from weighed combinations of
terms.
Data Collection Methods for Building a Free Response Training SimulationMelissa Moody
Master of Science in Data Science capstone project researchers Vaibhav Sharma, Beni Shpringer, and Michael Yang, along with UVA School of Engineering M.S. student Martin Bolger and Ph.D. students Sodiq Adewole and Erfaneh Gharavi, sought to develop new methods for collecting, generating, and labeling data to aid in the creation of educational, free-input dialogue simulations.
The sarcasm detection with the method of logistic regressionEditorIJAERD
The prediction analysis is approach which may predict future possibilities. This research work is based on the
sarcasm detection from the text data. In the previous time SVM classification is applied for the sarcasm detection. The SVM
classifier classifies data based on the hyper plane which give low accuracy. To improve accuracy for sarcasm detection
logistic regression is applied during this work. The existing and proposed techniques are implemented in python and results
are analysed in terms of accuracy, execution time. The proposed approach has high accuracy and low execution time as
compared to SVM classifier for sarcasm detection.
A simplified classification computational model of opinion mining using deep ...IJECEIAES
Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
There are numerous ways to analyse the web information, generally web substance are housed in
large information sets and basic inquiries are utilized to parse such information sets. As the requests
expanded with time, mining web information amended to meet challenging task in a web analysis.
Machine learning methodologies are the most up to date one to go into these analysis forms. Different
approaches like decision trees, association rules, Meta heuristic and basic learning methods are embraced
for making web data appraisal and mining data from various web instances. This study will highlight these
approaches in perspective of web investigation. One of the prime goals of this exploration is to investigate
more data mining approaches alongside machine learning systems, and to express emerging collaboration
of web analytics with artificial intelligence.
Sentiment analysis in SemEval: a review of sentiment identification approachesIJECEIAES
Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
A FRAMEWORK FOR SUMMARIZATION OF ONLINE OPINION USING WEIGHTING SCHEMEaciijournal
Recently there is wide use of social media includes various opinion sites, complaints sites, government
sites, question-answering sites, etc. through which customer get services, opinion, information, etc. but
because of this there is more and more use of these social media right now so huge amount of data will be
created, from this huge data people get confused while taking any decision about particular problem or
services. For example, customer wants to purchase a product at that time he/she want the previous
customer feedback or opinion about that product. But if there is lots of opinion available for particular
product then that customer get confused while taking decision whether purchase that product or not. In this
case there is a need of summarization concept means that only show the short and concise manner
summary about service or product so that customer or organization easily understand and able to take
right decision fast. Our proposed framework creating such summary which contain three main phases or
steps. Firstly preprocessing is done in that stop words are removed and stemming is performed. In second
phase identify frequent features using two techniques weight constraint and association rule and at the last
phase it find semantics and generate the summary so that customer will able to take step without confusion.
A FRAMEWORK FOR SUMMARIZATION OF ONLINE OPINION USING WEIGHTING SCHEMEaciijournal
Recently there is wide use of social media includes various opinion sites, complaints sites, government
sites, question-answering sites, etc. through which customer get services, opinion, information, etc. but
because of this there is more and more use of these social media right now so huge amount of data will be
created, from this huge data people get confused while taking any decision about particular problem or
services. For example, customer wants to purchase a product at that time he/she want the previous
customer feedback or opinion about that product. But if there is lots of opinion available for particular
product then that customer get confused while taking decision whether purchase that product or not. In this
case there is a need of summarization concept means that only show the short and concise manner
summary about service or product so that customer or organization easily understand and able to take
right decision fast. Our proposed framework creating such summary which contain three main phases or
steps. Firstly preprocessing is done in that stop words are removed and stemming is performed. In second
phase identify frequent features using two techniques weight constraint and association rule and at the last
phase it find semantics and generate the summary so that customer will able to take step without confusion.
A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
APPROXIMATE ANALYTICAL SOLUTION OF NON-LINEAR BOUSSINESQ EQUATION FOR THE UNS...mathsjournal
For one dimensional homogeneous, isotropic aquifer, without accretion the governing Boussinesq
equation under Dupuit assumptions is a nonlinear partial differential equation. In the present paper
approximate analytical solution of nonlinear Boussinesq equation is obtained using Homotopy
perturbation transform method(HPTM). The solution is compared with the exact solution. The
comparison shows that the HPTM is efficient, accurate and reliable. The analysis of two important aquifer
parameters namely viz. specific yield and hydraulic conductivity is studied to see the effects on the height
of water table. The results resemble well with the physical phenomena.
Analyzing sentiment system to specify polarity by lexicon-basedjournalBEEI
Currently, sentiment analysis into positive or negative getting more attention from the researchers. With the rapid development of the internet and social media have made people express their views and opinion publicly. Analyzing the sentiment in people views and opinion impact many fields such as services and productions that companies offer. Movie reviewer needs many processing to be prepared to detect emotion, classify them and achieve high accuracy. The difficulties arise due of the structure and grammar of the language and manage the dictionary. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Propose an innovative formula to compute the polarity score for each word occurring in the text and find it in positive dictionary or negative dictionary we have to remove it from text. After classification, the words are stored in a list that will be used to calculate the accuracy. The results reveal that the system achieved the best results in accuracy of 76.585%.
Sentimental classification analysis of polarity multi-view textual data using...IJECEIAES
The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats. Besides, in the case of the textual data exists as multi-view or unstructured data formats, the hybrid and integrated analysis efforts of text data mining algorithms are vital to get helpful results. The objective of this research is to enhance the knowledge discovery from sentimental multi-view textual data which can be considered as unstructured data format to classify the polarity information documents in the form of two different categories or types of useful information. A proposed framework with integrated data mining algorithms has been discussed in this paper, which is achieved through the application of X-means algorithm for clustering and HotSpot algorithm of association rules. The analysis results have shown improved accuracies of classifying the sentimental multi-view textual data into two categories through the application of the proposed framework on online polarity user-reviews dataset upon a given topics.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
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Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
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Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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objective. As opinion mining is gaining importance in existing system, therefore, it is imperative to
emphasize over the inherent challenges in this area. The first challenge in opinion mining is to manage
the level of specification integrity which is because of presence of various spam messages, opinion from
non-expert, bias opinion, credibility of opinion, gap between opinion topic and keyword used to search it,
typographical errors, irrelevancy of opinion, and dependency of domain under sentiment analysis.
The second challenge in opinion mining is connected with the level of word used where the problems are
mainly related to the absence of contextual information, issues of word orientation, usage of verb and
adjective in the form of opinion words, and adoption of orthogonal words. The third challenge in opinion
mining is associated with level of language or sentences which is mainly caused due to usage of different
linguistic, usage of multiple writing styles, restriction of filtering classification, product opinion etc.
There are many existing tools to carryout opinion mining e.g. Opinion Observer, Opinion Finder, Natural
Language Toolkit, Apache OpenNLP, LingPipe, WebFountain, Review Seer Tool, etc. [7, 8].
There is no doubt of many number of dedicated research work being carried out towards enhancing
the performance of opinion mining [9, 10], however, they are mainly localized to perform a complicated
mining operation over a static data. With the evolution of cloud computing and services offered by it,
the scale and size of the opinion data is exponentially increasing. This generates a highly unstructured
opinion data that is quite challenging to perform many further processing and they cannot be stored in
conventional storage units too. Therefore, the contribution of the proposed study is to present a framework
that can perform a novel opinion mining such that its output of extracted knowledge could be offered as
services. The proposed system uses a light weight design methodologies for this purpose and the discussion
has been carried out towards it. The organization of the paper is as follows: Section 1(a) discusses about
the existing literatures where different techniques are discussed for detection schemes used in power
transmission lines followed by discussion of research problems in Section 1(b) and proposed solution in 1(c).
Section 2 discusses about algorithm implementation followed by discussion of result analysis in Section 3.
Finally, the conclusive remarks are provided in Section 4.
a. Background
This section discusses about the recent work being carried out towards sentiment analysis over
the opinion of varied forms using different approaches. Study towards opinion mining over the social
network is carried out by Riquelme et al. [11]. Existing system was also recorded to use ontology-based
approach for carrying out opinion mining as seen in the work of Siddiqui et al. [12]. The study has also used
semantics using mathematical modeling towards facilitating feature engineering. Study towards sentiment
analysis was carried out the Xu et al. [13] where memory-based approach is used along with the classification
approach of neural network. Similar neural-network based approach was also implemented in the study of
Yu et al. [14] who have carried out extraction of opinion terms with an objective to find correlation between
different tasks.
The work of Huynh et al. [15] have used big data approach and convolution neural network over
opinion mining from social network using Vietnamese language. The work of He et al. [16] has used binary
mechanism for classifying opinion from online network in order to identify potential node. Classification
problems associated with opinion from social network was investigated by Fernandes et al. [17].
Consideration of metadata could further enrich the analysis process that is proven in the work of
Kren et al. [18] where opinion are extracted from the multimedia content where five different classification is
carried out over the data. A specific focus over influential node and its possible connection with the social
network is carried out by Mohammadinejad et al. [19]. The study has also used consensus factor for better
modeling perspective.
The work of Mullick et al. [20] presents a technique to extract particular feature from the social
network using learning-based technique. Usage of genetic algorithm was seen in the work of Iqbal et al. [21]
where the focus was over reduction of the feature. A discrete model was constructed for computing social
opinion by Wu et al. [22] where voting-based approach was used for performing predictive mining analysis.
The work of Zhang and Zhong [23] has carried out mining of the trust factor connected with two individual
using sentiment analyses using shortest path. AskariSichani and Jalili [24] have investigated over complex
network for formulating opinion using maximum-a-posteriori process considering identification of influential
node. Lv et al. [25] have used Markov modeling in order to perform opinion mining over essential data
resources. The study has also utilized belief propagation algorithm for this purpose. Zuo et al. [26] have
implemented aspect-based knowledge extraction process using Dirichlet processes along with the involuntary
indexing mechanism for differentiating multiple opinions. Zhou et al. [27] have considered language specific
opinion mining approach where a co-ranking algorithm is presented. The work of computing sentiments was
carried out by Jiang et al. [28] considering news dataset from social network using semantics. Adoption of
latent dirichlet allocation towards sentiment analysis was seen in work of Zhang and Chow [29] while
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consideration of human-based interaction was seen in work of Clavel and Callejas [30]. The next section
briefs of the cumulative open end issues in existing approaches towards opinion mining.
b. The research problem
The significant research problems are as follows:
- Existing approaches uses complex mining approach which induces delay in processing that can be
applied for offline analysis and not for online analysis.
- The problem of data transformation that is essential prior to storing the data over cloud considering
the various fields of opinion is never considered in any prior work.
- Adoption of machine learning approach towards optimization has higher dependencies over training
data which is highly time consuming in the process of yielding outcomes.
- Majority of the existing mining approach are resource dependent and not much distributed where
chances to apply an effective transformation technique is challenging.
Therefore, the problem statement of the proposed study can be stated as “Developing a framework towards cost
effective mining of the opinion data using semantic-based sentiment analysis is quite a challenging work”.
c. The proposed solution
The core goal of the proposed system is to develop a novel framework that could facilitate
processing the unstructured opinion from e-commerce application in order to carry out extraction of
knowledge. The extracted knowledge is delivered in the form of service and hence the framework is coined
as opinion data as a Service. The scheme of the service as per the proposed system is diagrammatically
shown in Figure 1.
Customer
Opinion
Algorithm Mined data
Algorithm for
Distributed Data
Aggregation
Algorithm for
Obtaining
Indexed Values
Algorithm for semantic-based
Knowledge Extraction
Figure 1. Adopted scheme of proposed system
The main focus of the implementation of the proposed system is that it offers opinion-as-a-service.
In order to do this implementation, the proposed system presents a comprehensive framework of highly
distributed scheme where multiple forms of unstructured data is aggregated unlikely the existing system.
The prime emphasis is to offer a discrete transformation of the unstructured opinion data and make it highly
structured so that it can be effectively stored in the distributed cloud storage units. The proposed scheme in
Figure 1 enables acceptance of different types of opinion data from the e-commerce application which are in
the form of text. The study considers that there are possibilities of massive number of opinion in large scale
from distributed system which is not feasible to be stored in cloud units effectively. Therefore, the proposed
system utilizes a novel indexing mechanism which is used to construct and retain a definite metadataof
the incoming stream of data.
All the incoming stream of the data is maintained in the form of temporary buffer where the samples
of the stream are forwarded to the algorithm over cloud in order to construct a progressive transformed data.
The algorithm running over the cloud clusters are developed in order to carry out following operation
sequentially viz. i) the algorithm initially perform aggregation of distributed data with primary indexing,
ii) the algorithm extracts all the indexed values from the incoming data index in the form of file objects with
multiple attributes, and iii) the algorithm also applies a semantic-based approach in order to perform
a correlation-based extraction of the mined data in the form of knowledge. One of the interesting
contributions of the proposed study is that it offers a user-friendly delivery of the knowledge from the mined
opinion. Another essential contribution of the proposed study is that it doesn’t store up the raw data of
opinion text but it retains only the mined information. All the processed and intermediate data are stored in
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a temporary buffer system which is released from its memory once the final outcome in terms of knowledge
is released to the user as the response of query generated by the user. The next section discusses about
the system design involved in the proposed study.
2. SYSTEM IMPLEMENTATION
The core agenda of the proposed system is to design and develop a mechanism that is capable of
extracting the knowledge from the online log files of the customer opinion. The implementation of
the proposed analytical operation is carried out using a novel semantic-based concept over the opinion data.
This section overviews different essential information associated with the system implementation with
respect to assumption-dependencies, implementation strategy, and execution flow.
2.1. Assumption and dependencies
The primary assumption of the proposed system is that there is a dedicated enterprise application
that could pull out the customer opinion data from different electronic commerce portals/applications from
a specific region of customer opinion and not all the data. The secondary assumption of the proposed system
is that all the customer opinion data could be related to different commercial domain connected with varied
product or services. The tertiary assumption of implementation is that all the customer opinion data is in
the form of text explicitly. Moreover, the study assumes that there are good possibilities of the data to be
highly unformatted as well as unstructured owing to the distributed traffic system over cloud as well as peak
traffic condition owing to massive concurrent users. The prime dependency of the proposed system is that
a specific domain of customer opinion is required in order to perform accurate assessment although
the implemented model is capable of analyzing any data from any commercial domain of customer opinion.
2.2. Implementation strategy
The actual idea of the proposed implementation is to perform online (or instantaneous) analysis of
the incoming streams of data (customer opinion). However, this opinion originates from multiple sources of
the e-commerce applications which are required to be gathered in one place without which performing
analysis is not feasible to be carried out. Therefore, the proposed system develops a distributed environment
in order to take the input of data from its distributed origination point. The next step is to carry out distributed
data aggregation with proper indexing of an incoming stream of data followed by identification of the core
file objects. The next process is to extract the header files, which represents the field of the essential value of
the customer data and reposit it over the cloud distributed storage. Finally, the proposed system develops
a novel semantic indexing that is used for correlating the values of the customer opinion data in order to
extract the knowledge from the indexed aggregated data. The complete process is highly sequential and
hence the execution is highly progressive leading to faster process of transforming. Hence, a simple and
faster process of analyzing the customer opinion data is obtained whose outcome is offered as a service.
2.3. Execution flow
The proposed system is implemented using divide and conquers rule where the complete problem
of extracting specific knowledge from the customer opinion data in the form of text over highly distributed
and collaborative system. The complete execution is carried out using three sequential algorithms viz.
i) Algorithm for distributed data aggregation, ii) algorithm for obtaining indexed values, and iii) algorithm
for semantic-based knowledge extraction. The discussions of the algorithms are as follows:
2.3.1. Algorithm for distributed data aggregation
It is to be noted that proposed system is actually an analytical design where it is essential to develop
a highly distributed terminals of generation of customer data. Therefore, this algorithm is basically
responsible for constructing a distributed environment that can facilitate data aggregation in order to obtain
indexed aggregated data for better data transformation leading to effective analysis of distributed data.
Algorithm for Distributed Data Aggregation
Input: n (selected opinion-files from stream)
Output: dagg (aggregated data)
Start
1. For i=1:n
2. dindexf(di)
3. daggg1(di, dindex)
4. daggg2(dagg)
5. End
End
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This algorithm takes the input of n (selected opinion-files from stream) that after processing yields
dagg (aggregated data). The algorithm constructs different terminals in distributed order that takes the input of
various opinion file and thereby act as the source of origination point of data n (Line-1). The next process is
to apply a function f(x) to the individual data di from each source point n (Line-2). This operation is used for
indexing all the incoming stream of data which is maintained in the form of matrix dindex (Line-2). The next
step is to further apply a function g1(x) for all the individual data di considering the generated data index dindex
in prior step to construct aggregated data dagg (Line-3). Finally, a discrete function g2(x), which is refinement
of g1(x) generates a file object of the data with its index to finally confirmed the indexed aggregated data dagg
(Line-4). The flow of the process involved in proposed algorithm is as follows. The Figure 2 illustrates
process flow of algorithm for distributed data aggregation.
Number of
Opinion files
Primary
indexing
Aggregated
data
File
objects
dagg
Figure 2. Process flow of algorithm for distributed data aggregation
2.3.2. Algorithm for obtaining indexed values
This algorithm performs an essential operation that is related with the further indexing operation of
the incoming aggregated data with respect to essential attributes of the file objects. Without this algorithm,
the system will not be able to perform indexing of explicit area of the corpus present in the discrete file
objects that will be further subjected for analysis.
Algorithm for Obtaining Indexed Values
Input: m (number of file objects)
Output: tbuff (memory with indexed value)
Start
1. For i=1: m
2. eobjc(dagg,m)
3. αfobjEncobj(encID)
4. βfobjextract(φ)
5. γfobjextract (val(φ+1))
6. End
7. Store αfobj, βfobj in cloud
8. tbuffindexed(γfobj)
End
The system constructs an object of the entire file (individual aggregated data dagg) and performs
further processing. The algorithm takes the input of m (number of file objects) that after processing yields tbuff
(memory with indexed value). Each file object consist of an explicit fields in the form of headers and
separator that are required to be stored in a cloud storage template so that it doesn’t have to store the same
everytime for the incoming data. The algorithm applies a function c(x) for the aggregated data with respect to
number of file objects m in order to generate an encoded object eobj of the file object (Line-2). The encoded
version of the file objects allows more user-friendly representation of the corpus of opinion data.
The algorithm than performs extraction of all the necessary fields in the individual file objects.
The first attribute within the file object is header files αfobj where the encoded objects are extracted
and retained with respect to discrete identity of encoded data encID (Line-3). The next part of the algorithm is
about extracting the separator φ used for distinguishing the header object and encoded value of the respective
header object βfobj (Line-4). Finally, the algorithm extract the value which is immediately after the separator
object and it is stored in a different matrix γfobj (Line-5). A closer look into the algorithmic steps will show
that objects like αfobj and βfobj are constants for every incoming data stream and is not required to be
processed everytime and hence, these two attributes are directed keyed in the cloud storage system which
will be further used for only indexing the respective value-based file objects γfobj. Figure 3 shows process
flow of this algorithm.
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Aggregated
data
File
objects
dagg
αfobj
βfobj
γfobj Temp buffer
Metadata
mgmt
(indexing)
Figure 3. Algorithm for obtaining indexed values
2.3.3. Algorithm for semantic-based knowledge extraction
This algorithm is responsible for extracting the knowledge from the third file object i.e. γfob obtained
from prior algorithm using semantics. The idea is also to construct a very simplified semantics that uses
correlation-based approach to extract the significant corpus attribute from the values of third file object as
a representation of knowledge. The potential benefit of this algorithm is its applicability towards any domain
of products/services of customer opinion which eliminates any chances of dependencies of lexicals unlike
existing approaches of mining.
Algorithm for semantic-based Knowledge Extraction
Input: tbuff(memory with indexed value)
Output: h(core-semantic)
Start
1. For k=1: tbuff
2. For z1=1:h1 //POS
3. For z2=1:h2
4. y=μ(cseg, h1)
5. If ∑y≥1
6. flag h
7. End
8. End
9. obtain h
10.End
End
This algorithm takes the input of tbuff (memory with indexed value) for giving an outcome of h
(core-semantic) (Line-1). The algorithm constructs various semantics h1 which are all possible representation
of corpus attributes (Line-2). These attributes are modifiable and the algorithm constructs further checks for
all h2 which are basically used for separating each corpus attributes in distinguished way and stored back in
matrix y (Line-4). The algorithm then performs string comparison for all the y matrix with the partitioned
corpus attributes in order to find the appropriate semantic h (Line-6 and Line-9). The Figure 4 presents
the algorithm for semantic-based knowledge extraction.
αfobj
βfobj
γfobj
Temp buffer
Metadata
mgmt
(indexing)
Apply
semantic
Indexing
Values
Extracted
Knowledge
Figure 4. Algorithm for semantic-based knowledge extraction
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3. RESULTS ANALYSIS
The proposed system uses the standard dataset of product review data from Amazon that is
frequently used for sentiment analysis [31]. The format of the data is reconfirmed to be taking the shape of
big data by referring publically available big data [32]. Hence, a synthetic data is constructed for opinion
mining. The dataset consists of 5500 plain text files with multiple reviews of specific products. This dataset
is taken as an input for the implementation of the proposed system that is carried out in MATLAB. The study
of the proposed system is compared with the standard mining approach e.g. supervised learning and
dictionary-based learning approach.
The analysis is carried out with respect to response time, memory consumption, and accuracy as
the performance parameters. The study outcome shows that the proposed system is comparatively better than
existing approach of opinion mining for sentiment analysis. The prime reason behind better response time is
that proposed system doesn’t have any dependency to carry out iterative training operation unlike
the existing approach. Moreover, the complete process is highly progressive and not recursive which reduces
the cumulative time of operation as shown in Figure 5(a). It can also be seen that proposed system also offers
lower memory consumption in contrast to existing approaches of opinion mining. It is because of the fact that
proposed system make use of temporary memory system where the processing takes place and only the final
mined data is stored. This phenomenon not only optimize the storage space in data center over cloud but also
offers faster processing of the queries towards leading to output of mined data as shown in Figure 5(b). Apart
from this, the proposed system also offers higher accuracy score towards mining performance as it makes use
of context that are formulated from the semantics used in the proposed system as shown in Figure 5(c). These
performance outcomes of the proposed system offers a robust confirmation that in order to relay the services
in the form of mined opinion. Another interesting point of the outcome is that it is capable to mine any form
of products or services and hence they are highly applicable for heterogeneous data too. It also offers
a flexibility to perform mining based on specific domain of query system e.g. degree of satisfaction, products
in higher demands, customer buying behavior, overall utilization score of products/services undertaken by
the user. Hence, the proposed system offers a highly cost effective computational model that is capable of
delivering opinion as a service with higher accuracy.
(a) (b)
(c)
Figure 5. Comparative analysis, (a) response time, (b) memory consumption, (c) accuracy
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4. CONCLUSION
Opinion mining is gaining proliferation owing to its importance in almost every field of product and
service delivery. From the past decade, there has been various research work carried out towards opinion
mining and sentiment analysis, however, they are all concerned about centralized system of applying analytics.
Inclusion of distributed computing leads to various challenges in the mining process as well as in data
aggregation process too. Therefore, the proposed system introduces a novel framework with following
contribution viz. i) the proposed system is capable of synchronizing all the incoming streams of data of
opinion and perform a structured aggregation, ii) the proposed system can also perform a perfect indexing of
all the file objects followed by extracting entered values of direct representation of opinion, and iii) proposed
system applies a very unique mechanism of semantics-based knowledge discovery process which is not only
fast but also makes the design free from usage of lexical unlike any existing mining approach.
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BIOGRAPHIES OF AUTHORS
Smt. Rajeshwari.D, working as Asst. Professor, Department of Information Science and
Engineering, NIE Institute of Technology, Mysore, India. She has completed her M.Tech from
Visvesvaraya Technological University Belagavi, India. She is pursuing her PhD from
Visvesvaraya Technological University Belagavi, India. Her areas of interest are Data Science,
Compiler Design, and Automata Theory. She has around 15 years of teaching experience.
Dr. Vagdevi S, Professor, Department of Information Science and Engineering, Dayananda
Sagar Academy of Technology and Management, Bengaluru, India. She has around 30 years of
work experience. She has published 18 international journal, 7 international conference and 11
national conference papers.