Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
IMPROVED SENTIMENT ANALYSIS USING A CUSTOMIZED DISTILBERT NLP CONFIGURATIONadeij1
Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner)
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.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
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
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.
IMPROVED SENTIMENT ANALYSIS USING A CUSTOMIZED DISTILBERT NLP CONFIGURATIONadeij1
Social media and other communication platforms have become extremely popular in last few years. User comments made on such portals contain valuable information about users’ attitudes. With the advent of Natural Language Processing (NLP) algorithms, it is now possible to analyze the polarity in such comments at scale. Sentiment analysis models use machine learning which can be divided into two kinds, supervised and unsupervised learning. Supervised models train models on pre-labeled datasets and predict the labels from a subset of the data. Unsupervised sentiment analysis tools utilize pre-trained models, using them to cluster unlabeled data. Recent research indicates Bidirectional Encoder Representations from Transformers (BERT) is useful in sentiment analysis tasks. Using a restaurant review dataset specific to highlighting different sentiments of positivity and negativity, this paper presents results from a comparative study of NLP techniques for sentiment analysis. The superiority of a fine-tuned distilBERT model is proven through a systematic experimental approach. Sentiment polarity coupled with adjustable neural network configurations makes distilBERT based models more sensitive to sentiment. Compared to conventional LSTM (Long Short-Term Memory) NLP approaches with accuracies of 78%, distilBERT achieves an accuracy of 92.4%, much better than 72.3%, the result of VADER (Valence Aware Dictionary and sEntiment Reasoner)
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.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
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
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.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
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.
Semantic Annotation Framework For Intelligent Information Retrieval Using KIM...dannyijwest
Due to the explosion of information/knowledge on the web and wide use of search engines for desired
information,the role of knowledge management(KM) is becoming more significant in an organization.
Knowledge Management in an Organization is used to create ,capture, store, share, retrieve and manage
information efficiently. The semantic web, an intelligent and meaningful web, tend to provide a promising
platform for knowledge management systems and vice versa, since they have the potential to give each
other the real substance for machine-understandable web resources which in turn will lead to an
intelligent, meaningful and efficient information retrieval on web. Today,the challenge for web community
is to integrate the distributed heterogeneous resources on web with an objective of an intelligent web
environment focusing on data semantics and user requirements. Semantic Annotation(SA) is being widely
used which is about assigning to the entities in the text and links to their semantic descriptions. Various
tools like KIM, Amaya etc may be used for semantic Annotation.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
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.
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.
Recommendation based on Clustering and Association RulesIJARIIE JOURNAL
Recommender systems play an important role in filtering and customizing the desired information.
Recommender system are divided into 3 categories i.e collaborative filtering , content-based filtering, and hybrid
filtering and they are the most adopted techniques being utilized in recommender systems. The paper mainly
describe about the issues of recommendation system.The main aim of paper is to recommend the suitable items to
the user, so for recommending the suitable items a better rule extraction is needed.Thus for better rule extraction
Association mining is applied .The clustering method is also applied here to cluster the data based on similar
characteristics .The propose methods try to eliminate certain problems such as sparsity, cold-start problem. So to
overcome the certain problem association mining over clustering is used
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
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.
Semantic Annotation Framework For Intelligent Information Retrieval Using KIM...dannyijwest
Due to the explosion of information/knowledge on the web and wide use of search engines for desired
information,the role of knowledge management(KM) is becoming more significant in an organization.
Knowledge Management in an Organization is used to create ,capture, store, share, retrieve and manage
information efficiently. The semantic web, an intelligent and meaningful web, tend to provide a promising
platform for knowledge management systems and vice versa, since they have the potential to give each
other the real substance for machine-understandable web resources which in turn will lead to an
intelligent, meaningful and efficient information retrieval on web. Today,the challenge for web community
is to integrate the distributed heterogeneous resources on web with an objective of an intelligent web
environment focusing on data semantics and user requirements. Semantic Annotation(SA) is being widely
used which is about assigning to the entities in the text and links to their semantic descriptions. Various
tools like KIM, Amaya etc may be used for semantic Annotation.
Scalable recommendation with social contextual informationeSAT Journals
Abstract Recommender systems are used to achieve effective and useful results in a social networks. The social recommendation will provide a social network structure but it is challenging to fuse social contextual factors which are derived from user’s motivation of social behaviors into social recommendation. Here, we introduce two contextual factors in recommender systems which are used to adopt a useful results namely a) individual preference and b) interpersonal influence. Individual preference analyze the social interests of an item content with user’s interest and adopt only users recommended results. Interpersonal influence is analyzing user-user interaction and their specific social relations. Beyond this, we propose a novel probabilistic matrix factorization method to fuse them in a latent space. The scalable algorithm provides a useful results by analyzing the ranking probability of each user social contextual information and also incrementally process the contextual data in large datasets. Keywords: social recommendation, individual preference, interpersonal influence, matrix factorization
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.
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.
Recommendation based on Clustering and Association RulesIJARIIE JOURNAL
Recommender systems play an important role in filtering and customizing the desired information.
Recommender system are divided into 3 categories i.e collaborative filtering , content-based filtering, and hybrid
filtering and they are the most adopted techniques being utilized in recommender systems. The paper mainly
describe about the issues of recommendation system.The main aim of paper is to recommend the suitable items to
the user, so for recommending the suitable items a better rule extraction is needed.Thus for better rule extraction
Association mining is applied .The clustering method is also applied here to cluster the data based on similar
characteristics .The propose methods try to eliminate certain problems such as sparsity, cold-start problem. So to
overcome the certain problem association mining over clustering is used
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
Socially Shared Images with Automated Annotation Process by Using Improved Us...IJERA Editor
Objectives: The main objective of this research is to increase the semantic concepts prominently as well as
reduce the searching time complexity. This is also aimed to ensure the higher privacy with security and develop
the accurate privacy policy generation.
Methods: The existing method named as adaptive privacy policy prediction (A3P) is used to discover the best
available privacy policy for the user’s image being uploaded. The proposed method name as improved semantic
annotated markovian semantic Indexing (ISMSI) is used for retrieving the images semantically.
Findings: The proposed method achieves high performance in terms of greater accuracy values.
Application/Improvements: The proposed system is done by using semantic annotated markovian semantic
Indexing (ISMSI) approach. ISMSI method is used for identification of similarity as well as semantic annotated
images and improves the privacy significantly.
A Brief Survey on Recommendation System for a Gradient Classifier based Inade...Christo Ananth
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Study and Analysis of K-Means Clustering Algorithm Using RapidminerIJERA Editor
Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery) is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS) and analysing that data generated using RapidMiner(Datamining Software) and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.
Detailed Investigation of Text Classification and Clustering of Twitter Data ...ijtsrd
As of late there has been a growth in data. This paper presents a methodology to investigate the text classification of data gathered from twitter. In this study sentiment analysis has been done on online comment data giving us picture of how to discover the demands of a people. Ziya Fatima | Er. Vandana "Detailed Investigation of Text Classification and Clustering of Twitter Data for Business Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38527.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/38527/detailed-investigation-of-text-classification-and-clustering-of-twitter-data-for-business-analytics/ziya-fatima
Data Analytics Course Curriculum_ What to Expect and How to Prepare in 2023.pdfNeha Singh
In 2023, aspiring data analysts can expect comprehensive data analytics course curriculums covering essential topics like statistical analysis, data visualization, machine learning, and big data processing. To prepare for the course, brushing up on basic mathematics, programming, and data handling skills would be beneficial.
Novel Methodology of Data Management in Ad Hoc Network Formulated using Nanos...Drjabez
In Ad hoc Network of Nanosensors for Wastage detection, clustering assist in nodal communication and in organization of the data fetched by the nanosensors in the network. The attempt of traditional cluster formation techniques degraded the formation of cluster in a precise manner. The data from the nanosensors which act as the nodes of the network have to be distinctively added into the clusters. The dynamic path selection cluster would achieve this distinct addition by dynamically creating a path to the data as an initial process and then redirecting the data to their appropriate cluster based to the readied scheme.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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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.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
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
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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• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
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• 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.
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.
Application
• 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.
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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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
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/
Profile Analysis of Users in Data Analytics Domain
1. Profile Analysis of StackOverflow Users in Data
Analytics Domain
Sri Saranya. M, *Vigneshwari S, Jabez J, Vimali J S
School of Computing, Sathyabama Institute of Science and Technology, Chennai
Abstract— Data Analytics and Data Science is in the fast forward
mode recently. We see a lot of companies hiring people for data
analysis and data science, especially in India. Also, many
recruiting firms use stackoverflow to fish their potential
candidates. The industry has also started to recruit people based
on the shapes of expertise. Expertise of a personal is
metaphorically outlined by shapes of letters like I, T, M and
hyphen betting on her experiencein a section (depth) and
therefore the variety of areas of interest (width).This proposal
builds upon the work of mining shapes of user expertise in a
typical online social Question and Answer (Q&A) community
where expert users often answer questions posed by other
users.We have dealt with the temporal analysis of the expertise
among the Q&A community users in terms how the user/ expert
have evolved over time.
Keywords— Shapes of expertise, Graph communities, Expertise
evolution, Q&A community
I. INTRODUCTION
The Question and Answer Community is a place
where people can gain and share knowledge. It provides a
valuable resource that cannot be easily obtained using search
engines. Q&A communities can serve as a source to locate
experts in certain areas. StackOverflow is a one which allows
users to post questions which are answered by other users. The
users can also vote for an answer. StackOverflow in particular
are used by developers could help us in better improving
developer experience in using these sites. It would also help us
to understand how information within StackOverflow could be
useful to various software development activities, and the
community of knowledge is formed.
StackOverflow.com is one of the most popular Q&A
websites focused mainly on questions that are related to
programming languages but it also contains different domains
in the IT industry.
II. STATE OF ART
Xiaomo, present a three-phase framework that
consequently produces expertise profiles of online group
individuals. The three stages include a document-topic
relevance model, a user document association model and a
filtering strategy. LDA model is used to identify best
performances [5]. Riahi, in his paper the main objective is to
concentrate on discovering specialists for a recently posted
query. They route new questions to the best suited experts.
Here two models have been used known as LDA (Content of
User profile) and STM (Structure of profiles). The STM
performs better than LDA (Segmented Topic Model) [8].
A temporal study of experts in CQA and analyze the
changes over time. Here the Gaussian Mixture Model based
clustering algorithm is used to find the experts in the domain.
Probability of providing a best answer increases for experts &
it decreases for ordinary users over time.
III. MATERIALS & METHODS
A. Data collection
The data is collected from the StackOverflow Q&A
forum. Data Science domain is selected for Profile analysis of
users. Input dataset has records starting from May2014 to
December2016.
B. Data Cleaning
Data cleaning (Pre-processing) is an important and
critical step in Data mining process. Raw data that includes
noise, incomplete and inconsistent values which affect the
accuracy of data in analysis is need to be preprocessed to
improve the quality of data and also to achieve better
results[11-17]. The Null values are checked and removed
&duplicate records are also removedfrom the attributes.
Finally, the cleaned data is transformed for further data mining
process.
C. Data Preparation
Import the data from XML to CSV file format. Select
the relevant data that has good range values. Post data is
selected with the required fields such as Date, PostType Id,
User Id, Accepted AnswerId, and Answer Countfor EDA
analysis. Then data transformation was carried out. Create a
Calendar for the required period to find response time and
merge the calendar with the Post data for performing EDA
analysis.
D. Exploratory Data Analysis
The cleaneddata is used for Exploratory Data
Analysis. In EDA analysis we find the total number of
questions and answers in data science domain, number of
questions asked by each user, total number of answers given
by each user, maximum, minimum and average number of
answers for a question, number of accepted answers given by
each user. Based on the above information the further analysis
is performed.
IV. EXPERIMENTAL DESIGN
International Journal of Pure and Applied Mathematics
Volume 119 No. 7 2018, 565-570
ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)
url: http://www.ijpam.eu
Special Issue
ijpam.eu
565
2. R is a programming language and it is an
environment for statistical and graphical techniques. It is
highly extensible. R studio V3.3.1 is used for this data
analysis.
Algorithm for finding expert users
1. Load the dataset from the stack exchange Q&A community
2. Parse and read the dataset from XML to CSV file
3. Pre-processing: Clean, remove null values and duplicates
4. Perform Exploratory Data Analysis for the cleaned dataset
5. Use graph partitioning algorithms to find the communities
from the tag groups
6. Compare the partitioning algorithms to find the best communities
based on their modularity
7. Find the expertise profile and define shapes for the expert users
8. Perform the prediction for the evolution of experts
9. Evaluate the performance analysis for the dataset
10. Visualize the results
E. Finding Areas of expertise
The next part of this paper deals with finding the
areas of expertise within a community. The graph partitioning
approach is used to find the areas of expertise. Graph is
created using co-occurrence of tags given by users to the
questions. Modularity is calculated to find the connections
within the community. High modularity reflects dense
connections within communities in the graph & sparse
connections across communities in the graph. Sub
communities obtained by the partitioning algorithms are
known as Area of Expertise within the community. In this
paper several graph partitioning algorithms are used and a
comparison of these algorithms is also performed.
a) Girvan Newman Algorithm
In this algorithm, at each step the edge with the
highest betweenness is removed from the graph. Modularity of
the graph is computed for every division. Modularity value for
Girvan Newman algorithm for finding community is
0.007191502.
Fig.1: Graph for edge.betweeness.community detection
Fig.1 shows the graph for GirvanNewman and here
most of the members have fall in a single community.
b) Fast Greedy Algorithm
The algorithm is agglomerative. At each step two
groups merge & merging is decided by optimizing the
modularity. It is a fast algorithm but it doesn’t produce the
best overall community for the given data.
Fig.2: Fast greedy community detection
Fig.2 shows the graph for community detection using
fast greedy algorithm. Modularity of fast greedy is 0.2088943.
Modularity is good when compared to Girvan Newman but the
community formation is not properly split here. Thus Fast
Greedy algorithm is not acceptable for the given dataset.
c) Spin Glass clustering
This algorithm is based on Spin glass model. Here
the cluster size is defined as 5 to find the communities.
Fig.3: Spin glass community detection
The Fig.3 shows the graph for spin glass community
detection. Modularity of spin glass is 0.2221456 and it is less
than fast greedy but members are not divided properly in all
the 5 groups.
d) Multilevel Community
Multilevel community detection is based on the
modularity measure and hierarchical approach. In this
algorithm each vertex is assigned to a community on its own
and in every step, vertices are re-assigned to communities in a
local or in greedy way.
The Fig.4 shows the multilevel community detection
and here we got 5 communities and we can also see sparse
connections across communities.Modularity of multilevel
community is 0.2021255. So, multilevel approach is well
suited for the given dataset.
International Journal of Pure and Applied Mathematics Special Issue
566
3. Fig.4: Multilevel community detection
F) Finding Expertise patterns
Based on the communities obtained from the
multilevel graph partitioning approach the areas of expertise
have been obtained for the data science users. To find the
levels of expertise within an area it is defined as Low,
Medium and High.High- Users answering several questions &
providing several best answers. Medium- Users answering
several questions some of them being best answers. Low-
Users interest in some areas & providing some answers.
Shapes are assigned based on the range of number of
answers given by users. The shapes for the expert users can be
given as follows,Hyphen: Contributing in one or more areas
but not expertise in any area.I-shaped: Expertise in only one
area & no contribution in other areas.T-shaped: Expertise in
one area & also contributing in many areas.M-shaped:
Expertise in more than one areas then he is considered as
master in the community.
G) Evolution of Expertise of each profile
Figure 5: User Activity Graph
Fig.5 shows the distribution of profile users over a
period of 32 months data. Here the I-shaped users are
neglected because their contribution is very few in this period
of 32 months. So, it’s not shown in the above graph.
V. RESULTS & DISCUSSIONS
Generally prediction is used to forecast an event for
futuretime period. Several prediction approaches are used in
data analytics such as regression, time series etc. In this paper
prediction using time series is performed. It is used to predict
the future values based on previously observed values. Auto
ARIMA (Autoregressive Moving Average) model and
NNETAR (Neural Network) model is used in this prediction
analysis and the accuracy for prediction is also compared.
a) Hyphen-shaped users
The figure 6(a) shows the comparison of original
time series and the forecasted time series of Hyphen shaped
users as modelled by auto Arima.
Fig.6 (a) Auto Arima: Original Vs Predicted
Figure 6(b) shows the original time series followed
by the forecasted values of Hyphen shaped users using neural
network model.
Fig.6 (b) NNETAR: Original Vs Predicted
b) T-shaped users
The fig.7(a) shows the comparison of original time
series and the forecasted time series of T-shaped users as
modelled by auto Arima. Fig.7(b) shows the original time
series followed by the forecasted values of T-shaped users
using neural network model.
Fig.7(a) Auto Arima: Original Vs Predicted
International Journal of Pure and Applied Mathematics Special Issue
567
4. Fig.7 (b) NNETAR: Original Vs Predicted
c) M-shaped users
The fig.8(a) shows the comparison of original time
series and the forecasted time series for M-shaped users as
modelled by auto Arima.
Fig.8(a) Auto Arima: Original Vs Predicted
Fig.8 (b) shows the original time series followed by
the forecasted values of M-shaped users using neural network
model.
Figure 8(b) NNETAR: Original Vs Predicted
TABLE 1: Statistics of Training and Testing data using Auto ARIMA &
NNETAR
The above table 1 shows the evaluation metrics for
Hyphen shaped, T-shaped and M-shaped profile users
modelled by using auto Arima and neural networks. Among
the above, the model for forecasting the Hyphen and M-
shaped users is acceptable in both ARIMA & NNETAR model
and the model for forecasting the T-shaped users is relatively
acceptable in both the models since the MAPE value is high.
VI. CONCLUSION & FUTURE WORKS
In this paper, we obtained a tendency towards the preditction
of social Q & A forurms which involves experience in
learning.TheExploratory Data analysis has been done and the
results were shown. Tag groups were used to form the
Communities using graph. Then the shapes were given to
every user based on their answer count. We also extend this to
do temporal analysis of change in expertise shapes over time.
It can be extended to other communities also. For expert
identification, we have only considered number of answers.
We can extend this to include number of interactions between
the expert users and can also include number of upvotes to
decide the expertise.
ACKNOWLEDGMENT
The authors would like to acknowledge that this work has
been carried out at DST-FIST sponsored Cloud Computing
Lab (order Saction No. : SR/FST/ETI-364/2014 Dated: 21
November, 2014), School of Computing, Sathyabama
University.
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