Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
Data mining in web search engine optimizationBookStoreLib
This document presents a proposed approach for optimizing web search by incorporating user feedback to improve result rankings. The approach uses keyword analysis on the user query to initially retrieve and rank relevant web pages. It then analyzes user responses like likes/dislikes and visit counts to update the page rankings. Experimental results on sample education queries show how page rankings change as user responses increase likes for certain pages. The approach aims to provide more useful search results by better reflecting individual user preferences.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
IRJET - Re-Ranking of Google Search ResultsIRJET Journal
This document summarizes a research paper that proposes a hybrid personalized re-ranking approach to search results. It models a user's search interests using a conceptual user profile containing categories and concepts extracted from clicked results and a concept hierarchy. The user profile contains two types of documents - taxonomy documents representing general interests and viewed documents representing specific interests. A hybrid re-ranking process then semantically integrates the user's general and specific interests from their profile with search engine rankings to improve result relevance.
PageRank algorithm and its variations: A Survey reportIOSR Journals
This document provides an overview and comparison of PageRank algorithms. It begins with a brief history of PageRank, developed by Larry Page and Sergey Brin as part of the Google search engine. It then discusses variants like Weighted PageRank and PageRank based on Visits of Links (VOL), which incorporate additional factors like link popularity and user visit data. The document also gives a basic introduction to web mining concepts and categorizes web mining into content, structure, and usage types. It concludes with a comparison of the original PageRank algorithm and its variations.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
Data mining in web search engine optimizationBookStoreLib
This document presents a proposed approach for optimizing web search by incorporating user feedback to improve result rankings. The approach uses keyword analysis on the user query to initially retrieve and rank relevant web pages. It then analyzes user responses like likes/dislikes and visit counts to update the page rankings. Experimental results on sample education queries show how page rankings change as user responses increase likes for certain pages. The approach aims to provide more useful search results by better reflecting individual user preferences.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
IRJET - Re-Ranking of Google Search ResultsIRJET Journal
This document summarizes a research paper that proposes a hybrid personalized re-ranking approach to search results. It models a user's search interests using a conceptual user profile containing categories and concepts extracted from clicked results and a concept hierarchy. The user profile contains two types of documents - taxonomy documents representing general interests and viewed documents representing specific interests. A hybrid re-ranking process then semantically integrates the user's general and specific interests from their profile with search engine rankings to improve result relevance.
PageRank algorithm and its variations: A Survey reportIOSR Journals
This document provides an overview and comparison of PageRank algorithms. It begins with a brief history of PageRank, developed by Larry Page and Sergey Brin as part of the Google search engine. It then discusses variants like Weighted PageRank and PageRank based on Visits of Links (VOL), which incorporate additional factors like link popularity and user visit data. The document also gives a basic introduction to web mining concepts and categorizes web mining into content, structure, and usage types. It concludes with a comparison of the original PageRank algorithm and its variations.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
This document summarizes various approaches to web search personalization that have been proposed by researchers. It discusses 12 different approaches that use techniques such as building user profiles based on browsing history and click data, constructing personalized ontologies and taxonomies, re-ranking search results based on learned user interests, and updating user profiles based on implicit feedback during web browsing. The document concludes that personalized search is needed to better tailor search results to individual users and their contexts, and that continued research on innovative personalization techniques is important as the amount of online information grows.
Quest Trail: An Effective Approach for Construction of Personalized Search En...Editor IJCATR
This document discusses developing a personalized search engine for software development organizations. It proposes using semantic analysis and genetic algorithms to personalize search results. Semantic analysis resolves ambiguity in queries by understanding their meaning, while genetic algorithms use machine learning to better understand user preferences over time. Quest analysis is also used to identify the goal or task behind a user's search by analyzing search logs at the quest level rather than query or session levels. Together these approaches aim to increase search relevance for users in software organizations by creating group profiles based on domain or project rather than individual user profiles.
Personalized web search using browsing history and domain knowledgeRishikesh Pathak
This document proposes a framework for improving personalized web search by constructing an enhanced user profile using both the user's browsing history and domain knowledge. The enhanced user profile is used to better suggest relevant web pages to the user based on their search query. An experiment found that suggestions made using the enhanced user profile performed better than using a standard user profile alone. The framework involves modeling the user, re-ranking search results, and displaying personalized results based on the enhanced user profile.
IRJET-Model for semantic processing in information retrieval systemsIRJET Journal
This document proposes a model for semantic information retrieval that improves upon traditional keyword matching approaches. It involves three main components:
1. A crawling and indexing component that identifies websites and pages, extracts metadata, and generates a knowledge graph through semantic annotation.
2. A processing component that analyzes user queries and profiles to understand search intent, calculates semantic similarity between queries and indexed documents, and determines result relevance.
3. A presentation component that displays search results to users through both simple and advanced search interfaces, prioritizing the most relevant information based on the above processing.
The model is intended to address deficiencies in current Cuban web search by better understanding natural language queries and the contextual meaning of information through semantic technologies
IRJET- Text-based Domain and Image Categorization of Google Search Engine usi...IRJET Journal
This document discusses a proposed system for categorizing search engine results using conceptual clustering. The system analyzes the content of search results to extract relevant concepts, then uses a personalized conceptual clustering algorithm to generate a decision tree of query clusters. This tree can be used to identify categories for web pages and provide topically relevant results to users. The system aims to improve on traditional ranked search results by categorizing results based on the conceptual preferences and interests of individual users.
Certain Issues in Web Page Prediction, Classification and Clustering in Data ...IJAEMSJORNAL
Nowadays, data mining which is a part of web mining plays a vital role in various applications such as search engines, health care centers for extracting the individual patient details among huge database, analyzing disease based on basic criteria, education system for analyzing their performance level with other system, social networking, E-Commerce and knowledge management etc., which extract the information based on the user query. The issues are time taken to mine the target content or webpage from the search engines, space complexity and predicting the frequent webpage for the next user based on users’ behaviour.
Improving Ranking Web Documents using User’s Feedbacks...............................................................1
Fatemeh Ehsanifar and Hasan Naderi
A Survey on Sparse Representation based Image Restoration ............................................................... 11
Dr. S. Sakthivel and M. Parameswari
Simultaneous Use of CPU and GPU to Real Time Inverted Index Updating in Microblogs
.................................................................................................................................................................... 25
Sajad Bolhasani and Hasan Naderi
A Survey on Prioritization Methodologies to Prioritize Non-Functional Requirements ........................ 32
Saranya. B., Subha. R and Dr. Palaniswami. S.
A Review on Various Visual Cryptography Schemes ................................................................................ 45
Nagesh Soradge and Prof. K. S. Thakare
Web Page Access Prediction based on an Integrated Approach ............................................................. 55
Phyu Thwe
A Survey on Bi-Clustering and its Applications ..................................................................................65
K. Sathish Kumar, M. Ramalingam and Dr. V. Thiagarasu
Pixel Level Image Fusion: A Neuro-Fuzzy Approach ................................................................................ 71
Swathy Nair, Bindu Elias and VPS Naidu
A Comparative Analysis on Visualization of Microarray Gene Expression Data...................................... 87
Poornima. S and Dr. J. Jeba Emilyn
Web surfing for various purposes has become a habit of humans. Searching for information from the Internet today has been made easier by the widely available search engines. However, there are many search engines and their number is increasing. It is of considerable importance for the designer to develop quality search engines and for the users to select the most appropriate ones for their use. The Information quality linked through these searches is quite irregular. There are fair chances that the retrieved results are irreverent and belong to an unreliable source. In fact, most search engines are developed mainly for better technical performance and there could be a lack of quality attributes from the customers’ perspective. In this paper, we first provide a brief review of the most commonly used search engines, with the focus on existing comparative studies of the search engines. The paper also includes a survey conducted of 137 respondents where the identified user expectations will be of great help not only to the designers for improving the search engines, but also to the users for selecting suitable ones. The objective behind this study was also to find the reason behind poor precision and recall of so many available search engines. The study finally aims to enhance user search experience.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
This document summarizes a research paper that proposes a framework for personalized web search using query log and clickthrough data. The framework implements a re-ranking approach that combines user search context and browsing behavior to generate personalized search results with high relevance. The framework consists of five components: a request handler, query processor, result handler, event handler, and response handler. The result handler applies a re-ranking approach using query log and clickthrough data to personalize search results before returning them to the user. An evaluation found the framework and re-ranking approach to be effective for personalized web search and information retrieval.
This study compared the search results of Google, Yahoo, and Bing for queries related to health literacy. It found that the number of search results or "hits" varied between search engines and query types. For the single term "health literacy", Yahoo returned the most results, followed by Google then Bing. However, for phrase searches or those using Boolean operators, the search engine with the most results differed. An analysis of the first 40 websites for the search "health literacy" found that most were commercial or non-government organizations, with fewer from educational institutions or government sources. The study concluded that librarians can help users refine searches to find the most appropriate information sources given variations in search engine results and website sponsorships.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An agent-based collection development system is proposed that uses software agents to perform tasks related to selecting, acquiring, recording, and disseminating information for collection development. The system would use various agents like a Profile Agent to analyze faculty webpages and develop research interest profiles, a Citation Agent to identify publications and topics, a Search Agent to search resources and identify relevant works, and other agents to check the catalog, monitor usage, process interlibrary loans, and handle acquisitions. The agent system could automate many routine collection development tasks currently performed by librarians and help libraries better support faculty research needs.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
A Survey on Web Page Recommendation and Data PreprocessingIJCERT
In today’s era, as we all know internet technologies are growing rapidly. Along with this, instantly, Web page recommendations are also improving. The aim of a Web page recommender system is to predict the Web page or pages, which will be visited from a given Web-page of a website. Data preprocessing is one basic and essential part of Web page recommendation. Data preprocessing consists of cleanup and constructing data to organize for extracting pattern. In this paper, we discuss and focus on Web page Recommendation and role of data preprocessing in Web page recommendation, considering how data preprocessing is related to Web page recommendation.
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
The document is a curriculum vitae for Ram Kishor. It summarizes his career objective, strengths, weaknesses, and work experience. Some key points:
- Ram Kishor has over 15 years of experience in electrical and electronics engineering, currently working as an Account Head for customer services at Autometers Alliance Limited.
- His previous roles include positions at Autometers Alliance Limited, Hi-Rel Electronics, and Target Marketing in customer support, service, and sales.
- He has a diploma in electronics engineering and experience managing customer service teams, maintenance, installations, and business relations.
Minoo Pourhassan Shamchi's resume summarizes her educational and professional background. She holds a Ph.D in Biotechnology from Hacettepe University in Turkey, and has experience teaching at universities in Iran. Her research focuses on water treatment using algae, including the removal of dyes, pesticides, and herbicides. She has published numerous papers in international journals and presented her work at scientific conferences.
The document provides information about several important figures in computer science and technology. It discusses their major accomplishments and contributions, including inventing technologies like the computer mouse, the microprocessor, WiFi, Bluetooth, USB, and the World Wide Web. Many of them were pioneers in fields like artificial intelligence, computer programming, software development, internet infrastructure and more.
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
This document summarizes various approaches to web search personalization that have been proposed by researchers. It discusses 12 different approaches that use techniques such as building user profiles based on browsing history and click data, constructing personalized ontologies and taxonomies, re-ranking search results based on learned user interests, and updating user profiles based on implicit feedback during web browsing. The document concludes that personalized search is needed to better tailor search results to individual users and their contexts, and that continued research on innovative personalization techniques is important as the amount of online information grows.
Quest Trail: An Effective Approach for Construction of Personalized Search En...Editor IJCATR
This document discusses developing a personalized search engine for software development organizations. It proposes using semantic analysis and genetic algorithms to personalize search results. Semantic analysis resolves ambiguity in queries by understanding their meaning, while genetic algorithms use machine learning to better understand user preferences over time. Quest analysis is also used to identify the goal or task behind a user's search by analyzing search logs at the quest level rather than query or session levels. Together these approaches aim to increase search relevance for users in software organizations by creating group profiles based on domain or project rather than individual user profiles.
Personalized web search using browsing history and domain knowledgeRishikesh Pathak
This document proposes a framework for improving personalized web search by constructing an enhanced user profile using both the user's browsing history and domain knowledge. The enhanced user profile is used to better suggest relevant web pages to the user based on their search query. An experiment found that suggestions made using the enhanced user profile performed better than using a standard user profile alone. The framework involves modeling the user, re-ranking search results, and displaying personalized results based on the enhanced user profile.
IRJET-Model for semantic processing in information retrieval systemsIRJET Journal
This document proposes a model for semantic information retrieval that improves upon traditional keyword matching approaches. It involves three main components:
1. A crawling and indexing component that identifies websites and pages, extracts metadata, and generates a knowledge graph through semantic annotation.
2. A processing component that analyzes user queries and profiles to understand search intent, calculates semantic similarity between queries and indexed documents, and determines result relevance.
3. A presentation component that displays search results to users through both simple and advanced search interfaces, prioritizing the most relevant information based on the above processing.
The model is intended to address deficiencies in current Cuban web search by better understanding natural language queries and the contextual meaning of information through semantic technologies
IRJET- Text-based Domain and Image Categorization of Google Search Engine usi...IRJET Journal
This document discusses a proposed system for categorizing search engine results using conceptual clustering. The system analyzes the content of search results to extract relevant concepts, then uses a personalized conceptual clustering algorithm to generate a decision tree of query clusters. This tree can be used to identify categories for web pages and provide topically relevant results to users. The system aims to improve on traditional ranked search results by categorizing results based on the conceptual preferences and interests of individual users.
Certain Issues in Web Page Prediction, Classification and Clustering in Data ...IJAEMSJORNAL
Nowadays, data mining which is a part of web mining plays a vital role in various applications such as search engines, health care centers for extracting the individual patient details among huge database, analyzing disease based on basic criteria, education system for analyzing their performance level with other system, social networking, E-Commerce and knowledge management etc., which extract the information based on the user query. The issues are time taken to mine the target content or webpage from the search engines, space complexity and predicting the frequent webpage for the next user based on users’ behaviour.
Improving Ranking Web Documents using User’s Feedbacks...............................................................1
Fatemeh Ehsanifar and Hasan Naderi
A Survey on Sparse Representation based Image Restoration ............................................................... 11
Dr. S. Sakthivel and M. Parameswari
Simultaneous Use of CPU and GPU to Real Time Inverted Index Updating in Microblogs
.................................................................................................................................................................... 25
Sajad Bolhasani and Hasan Naderi
A Survey on Prioritization Methodologies to Prioritize Non-Functional Requirements ........................ 32
Saranya. B., Subha. R and Dr. Palaniswami. S.
A Review on Various Visual Cryptography Schemes ................................................................................ 45
Nagesh Soradge and Prof. K. S. Thakare
Web Page Access Prediction based on an Integrated Approach ............................................................. 55
Phyu Thwe
A Survey on Bi-Clustering and its Applications ..................................................................................65
K. Sathish Kumar, M. Ramalingam and Dr. V. Thiagarasu
Pixel Level Image Fusion: A Neuro-Fuzzy Approach ................................................................................ 71
Swathy Nair, Bindu Elias and VPS Naidu
A Comparative Analysis on Visualization of Microarray Gene Expression Data...................................... 87
Poornima. S and Dr. J. Jeba Emilyn
Web surfing for various purposes has become a habit of humans. Searching for information from the Internet today has been made easier by the widely available search engines. However, there are many search engines and their number is increasing. It is of considerable importance for the designer to develop quality search engines and for the users to select the most appropriate ones for their use. The Information quality linked through these searches is quite irregular. There are fair chances that the retrieved results are irreverent and belong to an unreliable source. In fact, most search engines are developed mainly for better technical performance and there could be a lack of quality attributes from the customers’ perspective. In this paper, we first provide a brief review of the most commonly used search engines, with the focus on existing comparative studies of the search engines. The paper also includes a survey conducted of 137 respondents where the identified user expectations will be of great help not only to the designers for improving the search engines, but also to the users for selecting suitable ones. The objective behind this study was also to find the reason behind poor precision and recall of so many available search engines. The study finally aims to enhance user search experience.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
This document summarizes a research paper that proposes a framework for personalized web search using query log and clickthrough data. The framework implements a re-ranking approach that combines user search context and browsing behavior to generate personalized search results with high relevance. The framework consists of five components: a request handler, query processor, result handler, event handler, and response handler. The result handler applies a re-ranking approach using query log and clickthrough data to personalize search results before returning them to the user. An evaluation found the framework and re-ranking approach to be effective for personalized web search and information retrieval.
This study compared the search results of Google, Yahoo, and Bing for queries related to health literacy. It found that the number of search results or "hits" varied between search engines and query types. For the single term "health literacy", Yahoo returned the most results, followed by Google then Bing. However, for phrase searches or those using Boolean operators, the search engine with the most results differed. An analysis of the first 40 websites for the search "health literacy" found that most were commercial or non-government organizations, with fewer from educational institutions or government sources. The study concluded that librarians can help users refine searches to find the most appropriate information sources given variations in search engine results and website sponsorships.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An agent-based collection development system is proposed that uses software agents to perform tasks related to selecting, acquiring, recording, and disseminating information for collection development. The system would use various agents like a Profile Agent to analyze faculty webpages and develop research interest profiles, a Citation Agent to identify publications and topics, a Search Agent to search resources and identify relevant works, and other agents to check the catalog, monitor usage, process interlibrary loans, and handle acquisitions. The agent system could automate many routine collection development tasks currently performed by librarians and help libraries better support faculty research needs.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
A Survey on Web Page Recommendation and Data PreprocessingIJCERT
In today’s era, as we all know internet technologies are growing rapidly. Along with this, instantly, Web page recommendations are also improving. The aim of a Web page recommender system is to predict the Web page or pages, which will be visited from a given Web-page of a website. Data preprocessing is one basic and essential part of Web page recommendation. Data preprocessing consists of cleanup and constructing data to organize for extracting pattern. In this paper, we discuss and focus on Web page Recommendation and role of data preprocessing in Web page recommendation, considering how data preprocessing is related to Web page recommendation.
Adaptive Search Based On User Tags in Social NetworkingIOSR Journals
This document summarizes an article about adaptive search based on user tags in social networking. It discusses using tags that users apply to images in social media sites like Flickr to improve image search and personalize results. It proposes using topic models to identify different meanings of ambiguous tags and a user's interests to display more relevant images. The framework involves reranking images based on aesthetics scores predicted from user comments, and using tag-based and group-based metadata to discover topics and personalize search results. Future work could further analyze community-generated metadata to identify interests and refine search algorithms.
The document is a curriculum vitae for Ram Kishor. It summarizes his career objective, strengths, weaknesses, and work experience. Some key points:
- Ram Kishor has over 15 years of experience in electrical and electronics engineering, currently working as an Account Head for customer services at Autometers Alliance Limited.
- His previous roles include positions at Autometers Alliance Limited, Hi-Rel Electronics, and Target Marketing in customer support, service, and sales.
- He has a diploma in electronics engineering and experience managing customer service teams, maintenance, installations, and business relations.
Minoo Pourhassan Shamchi's resume summarizes her educational and professional background. She holds a Ph.D in Biotechnology from Hacettepe University in Turkey, and has experience teaching at universities in Iran. Her research focuses on water treatment using algae, including the removal of dyes, pesticides, and herbicides. She has published numerous papers in international journals and presented her work at scientific conferences.
The document provides information about several important figures in computer science and technology. It discusses their major accomplishments and contributions, including inventing technologies like the computer mouse, the microprocessor, WiFi, Bluetooth, USB, and the World Wide Web. Many of them were pioneers in fields like artificial intelligence, computer programming, software development, internet infrastructure and more.
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
This CV summarizes the education and experience of Mahmoud Abd El Hady Mostafa Hassan. He has a Bachelor's Degree in Computer Engineering from Cairo University with very good grades. He has almost 4 years of experience as a Senior .NET and SharePoint developer. He has extensive experience designing and developing custom SharePoint 2013 applications using technologies like C#, ASP.NET, SQL Server and Visual Studio. His most recent role is as a Senior SharePoint Developer at Asset Technology Group where he develops custom SharePoint solutions for clients like STC Management Systems and King Fahd Special Hospital.
Electrocardiogram Denoised Signal by Discrete Wavelet Transform and Continuou...CSCJournals
One of commonest problems in electrocardiogram (ECG) signal processing is denoising. In this paper a denoising technique based on discrete wavelet transform (DWT) has been developed. To evaluate proposed technique, we compare it to continuous wavelet transform (CWT). Performance evaluation uses parameters like mean square error (MSE) and signal to noise ratio (SNR) computations show that the proposed technique out performs the CWT.
At the very heart of cognitive psychology is the idea of information processing. Cognitive psychology sees the individual as a processor of information, in much the same way that a computer takes in information and follows a program to produce an output.Cognitive psychology compares the human mind to a computer, suggesting that we too are information processors and that it is possible and desirable to study the internal mental / mediational processes that lie between the stimuli (in our environment) and the response we make.
The information processing paradigm of cognitive psychology views that minds in terms of a computer when processing information.
However, there are important difference between humans and computers. The mind does not process information like a computer as computers don’t have emotions or get tired like humans
This document provides an overview and analysis of the global economic outlook and discusses how falling oil prices and a rising US dollar are impacting consumer markets. Key points include:
- Falling oil prices are boosting consumer purchasing power but hurting oil-exporting economies. Prices may continue falling in the short term but rebound in 1-2 years as US production declines.
- The rising US dollar is disinflationary domestically but inflationary for other countries, posing risks for emerging markets with dollar-denominated debts. The dollar will likely continue rising in early 2015.
- China's economy is slowing as export markets weaken and efforts to curb shadow banking contribute to deceleration, though the
`A Survey on approaches of Web Mining in Varied Areasinventionjournals
There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate, ranking will be done automatically, clustering the feedback sessions. Web search engines have made enormous contributions to the web and society. They make finding information on the web quick and easy. However, they are far from optimal. A major deficiency of generic search engines is that they follow the ‘‘one size fits all’’ model and are not adaptable to individual users.
Enhanced Web Usage Mining Using Fuzzy Clustering and Collaborative Filtering ...inventionjournals
This document discusses an enhanced web usage mining system using fuzzy clustering and collaborative filtering recommendation algorithms. It aims to address challenges with existing recommender systems like producing low quality recommendations for large datasets. The system architecture uses fuzzy clustering to predict future user access based on browsing behavior. Collaborative filtering is then used to produce expected results by combining fuzzy clustering outputs with a web database. This approach aims to provide users with more relevant recommendations in a shorter time compared to other systems.
IDENTIFYING IMPORTANT FEATURES OF USERS TO IMPROVE PAGE RANKING ALGORITHMSZac Darcy
A Conversational Agent for the Web of Data, Journal of Web Semantics, Volume 37–38,
2016, Pages 64-85, ISSN 1570-8268.
[4] J. M. Kleinberg, (1999), Authoritative sources in a hyperlinked environment, Journal of the ACM
(JACM), 46(5), 604-632.
[5] L. Page, S. Brin, R. Motwani, and T. Winograd, (1999), The PageRank citation ranking: Bringing
order to the web. Technical Report, Stanford InfoLab.
[6] S. Chakrabarti, (2003), Min
Identifying Important Features of Users to Improve Page Ranking Algorithms dannyijwest
Increase in number of ontologies on Semantic Web and endorsement of OWL as language of discourse for the Semantic Web has lead to a scenario where research efforts in the field of ontology engineering may be applied for making the process of ontology development through reuse a viable option for ontology developers. The advantages are twofold as when existing ontological artefacts from the Semantic Web are reused, semantic heterogeneity is reduced and help in interoperability which is the essence of Semantic Web. From the perspective of ontology development advantages of reuse are in terms of cutting down on cost as well as development life as ontology engineering requires expert domain skills and is time taking process. We have devised a framework to address challenges associated with reusing ontologies from the Semantic Web. In this paper we present methods adopted for extraction and integration of concepts across multiple ontologies. We have based extraction method on features of OWL language constructs and context to extract concepts and for integration a relative semantic similarity measure is devised. We also present here guidelines for evaluation of ontology constructed. The proposed methods have been applied on concepts from food ontology and evaluation has been done on concepts from domain of academics using Golden Ontology Evaluation Method with satisfactory outcomes
IDENTIFYING IMPORTANT FEATURES OF USERS TO IMPROVE PAGE RANKING ALGORITHMSIJwest
Web is a wide, various and dynamic environment in which different users publish their documents. Webmining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users’ interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users’ interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
This document is a seminar report submitted for a master's degree that focuses on search engine optimization (SEO). It includes an abstract, introduction, sections on search engines and how they work, SEO basics, techniques for on-page and off-page optimization, challenges of SEO, and applications of SEO. The introduction discusses how search engines and SEO have become important for online marketing. Key sections explain the SEO process, on-page optimization including keywords and content, and off-page optimization such as links and social media. Challenges of measuring SEO success are also noted.
Performance of Real Time Web Traffic Analysis Using Feed Forward Neural Netw...IOSR Journals
This document discusses using feed forward neural networks and K-means clustering to analyze real-time web traffic. It proposes a technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The model uses a multi-layered network architecture with backpropagation learning to discover and analyze knowledge from web log data. It also discusses preprocessing the web log data through cleaning, user identification, filtering, session identification and transaction identification before applying the neural network and K-means algorithms.
The document describes a system called UProRevs that aims to personalize web search results based on the user's profile and interests. It does this by taking the results from a normal search engine, calculating the relevance of each result to the user's profile, and displaying the results along with this relevance score. The system generates user profiles based on information provided during registration and updates them over time based on the user's feedback on search results. It calculates relevance by comparing keywords from the user profile and web page, and weighting them based on their ranks in each profile. The goal is to provide more useful search results tailored to each individual user's perspective.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
This document summarizes techniques for improving web search results through web personalization. It discusses how web usage mining can be used to optimize information by monitoring user interaction histories and profiles. The proposed system aims to reduce manual user feedback by implicitly gathering preferences from behaviors like click-through rates and dwell times. It introduces an algorithm that calculates new ranking values for websites based on keyword matches and time spent on pages, and swaps ranks accordingly. This system provides personalized search results by continuously updating rankings based on implicit user interactions.
GOOGLE SEARCH ALGORITHM UPDATES AGAINST WEB SPAMieijjournal
Google has released many algorithm updates to combat web spam over the years:
- Updates like Panda and Penguin specifically target low-quality sites, thin content sites, and sites engaging in unethical SEO practices like link spamming.
- Other updates like Caffeine and Social Signals aim to incorporate more authoritative signals like social media mentions and site freshness to improve results.
- Google's goal is to balance economic incentives for spammers by keeping costs high for manipulation, while continually adapting their algorithms to mitigate new spam tactics.
The web has become a resourceful tool for almost all domains today. Search engines prominently use
inverted indexing technique to locate the web pages having the users query. The performance of inverted
index fundamentally depends upon the searching of keyword in the list maintained by search engine. Text
matching is done with the help of string matching algorithm. It is important to any string matching
algorithm to locate quickly the occurrences of the user specified pattern in large text. In this paper a new
string matching algorithm for keyword searching is proposed. The proposed algorithm relies on new
technique based on pattern length and FML (First-Middle-Last) character match. This proposed
algorithm is analysed and implemented. The extensive testing and comparisons are done with BoyerMoore, Naïve, Improved Naïve, Horspool and Zhu Takaoka. The result shows that the proposed
algorithm takes less time than other existing algorithm.
Semantically enriched web usage mining for predicting user future movementsIJwest
Explosive and quick growth of the World Wide Web has resulted in intricate Web sites, demanding
enhanced user skills and sophisticated tools to help the Web user to find the desi
red information. Finding
desired information on the Web has become a critical ingredient of everyday personal, educational, and
business life. Thus, there is a demand for more sophisticated tools to help the user to navigate a Web site
and find the desired
information. The users must be provided with information and services specific to
their needs, rather than an undiffere
ntiated mass of information.
For discovering interesting and frequent
navigation patterns from Web server logs many Web usage mining te
chniques have been applied. The
recommendation accuracy of solely usage based techniques can be improved by integrating Web site
content and site structure in the personalization process.
Herein, we propose Semantically enriched Web Usage Mining method (S
WUM), which combines the fields
of Web Usage Mining and Semantic Web. In the proposed method, the undirected graph derived from
usage data is enriched with rich semantic information extracted from the Web pages and the Web site
structure. The experimental
results show that the SWUM generates accurate recommendations with
integration of usage, semantic data and Web site structure. The results shows that proposed method is able
to achieve 10
-
20%
better accuracy than the solely usage based model, and 5
-
8% bet
ter than an ontology
based model.
This document summarizes a conference paper on using machine learning algorithms for static page ranking. It discusses how supervised learning algorithms like RankNet can be used to combine multiple static features, like page content, links, and popularity data, to generate page rankings. The paper finds this machine learning approach significantly outperforms traditional PageRank, providing more robust and less technology-biased rankings. Features, ranking methods, applications, and algorithms are described in detail. The conclusions recommend further experimentation with additional features and machine learning techniques to improve static page ranking.
The document describes a proposed algorithm called Visitors' Online Behavior (VOB) for tracing visitors' online behaviors to effectively mine web usage data. The VOB algorithm identifies user behavior, creates user and page clusters, and determines the most and least popular web pages. It discusses how web usage mining analyzes user behavior logs to discover patterns. Preprocessing techniques like data cleaning, user/session identification, and path completion are applied to web server logs to maximize accurate pattern mining. Existing algorithms are described that apply preprocessing concepts to calculate unique user counts, minimize log file sizes, and identify user sessions.
This document summarizes an approach to improve personalized ranking using associated edge weights. The key points are:
1) Personalized ranking aims to improve traditional search and retrieval by tailoring results to a user's interests. Authority flow approaches like PageRank and ObjectRank can be used for personalized ranking on entity-relationship graphs.
2) Edge-based personalization assigns different weights to edge types based on the user, affecting authority flow. The paper focuses on improving edge-based personalization by hybridizing the ScaleRank algorithm with k-means clustering.
3) ScaleRank approximates the DataApprox algorithm for ranking. The hybrid approach uses k-means clustering on ScaleRank output to further group related nodes,
A Survey on: Utilizing of Different Features in Web Behavior PredictionEditor IJMTER
As the web user increases day by day, there are many websites which have a large
number of visitors at the same instant. So handing of these user required different technique. Out of
these requirements one emerging field is next page prediction, where as per the user navigation
pattern different features has been studied and predict the next page for the user. By this overall web
server response time is reduce. In this paper a detailed study of the different researcher paper has
shown, there techniques outcomes and list of features utilization such as web structure, web log, web
content.
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effectiv
Similar to CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVAL (20)
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
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CONTENT AND USER CLICK BASED PAGE RANKING FOR IMPROVED WEB INFORMATION RETRIEVAL
1. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
DOI:10.5121/ijcsa.2015.5608 111
CONTENT AND USER CLICK BASED PAGE
RANKING FOR
IMPROVED WEB INFORMATION RETRIEVAL
Dhiliphan Rajkumar.T1
,Suruliandi.A2
and Selvaperumal.P3
1
Research Scholar,Department of Computer Science & Engineering Manonmaniam
Sundaranar University,Tirunelveli-627012, India
2
Professor,Department of Computer Science & EngineeringManonmaniam Sundaranar
University,Tirunelveli-627012, India and
3
Research Scholar, Department of Computer Science & Engineering, Manonmaniam
Sundaranar University,Tirunelveli-627012, India
ABSTRACT
Search engines today are retrieving more than a few thousand web pages for a single query, most of which
are irrelevant. Listing results according to user needs is, therefore, a very real necessity. The challenge lies
in ordering retrieved pages and presenting them to users in line with their interests. Search engines,
therefore, utilize page rank algorithms to analyze and re-rank search results according to the relevance of
the user’s query by estimating (over the web) the importance of a web page. The proposed work
investigates web page ranking methods and recently-developed improvements in web page ranking.
Further, a new content-based web page rank technique is also proposed for implementation. The proposed
technique finds out how important a particular web page is by evaluating the data a user has clicked on, as
well as the contents available on these web pages. The results demonstrate the effectiveness of the proposed
page ranking technique and its efficiency.
KEYWORDS
Web mining, World Wide Web, Search Engine, Web Page, Page Ranking.
1.INTRODUCTION
1.1.Back Ground
Information retrieval today is a challenging task. The amount of information on the web is
increasing at a drastic pace. Millions of users are searching for information they desire to have at
their fingertips. It is no trivial task for search engines to feed users relevant information. Today,
web use has increased across fields such as e-commerce, e-learning, and e-news. Naturally,
finding user needs and providing useful information are the goals of website owners [1].
Consequently, tracing user behaviour has become increasingly important. Web mining is used to
discover user behaviour in the past, including the content of the web and web pages a user wants
to view in the future. A search engine is a piece of software that act as an interface for users to
retrieve the data desired from the World Wide Web (WWW). As of now, the WWW is the largest
information repository for knowledge, and the quality of a page is defined based on user clicks. It
is precisely for that purpose that a lot of page ranking algorithms have been introduced. The page
ranking is an algorithm developed by Sergey Brin and Lawrence Page in 1998 and used by
Google search engine. The algorithm assigns a numerical value to each element in the WWW for
2. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
112
the purpose of measuring the relative importance of each page, the idea being to give a higher
page rank value to a page that is frequently visited by users. The ranking can be done in three
ways: keyword popularity, keyword-to-web page popularity, and web page popularity. Keyword-
based ranking focuses on the most popular keyword first. The keyword-to-web page popularity
records which pages have been selected for a user’s search query. The final one determines how
frequently a web page is selected by a user worldwide. Page ranking is a great concept that helps
determine the importance of a given page among a number of similarly indexed pages. It is
basically used to calculate scores of elements of a search system. Traditionally, that concept has
been widely accepted for web page ranking and organization of results. Therefore, a number of
data retrieval and search systems utilize this concept for organizing their results. The results
returned, for the same query at different times in search engines, are the same. In recent times,
search engines have been using the page ranking concept so that the values on the web page are
not identical at all times, as interest in the page might then vary or change. Table 1 displays the
results of user satisfaction with relevant search engines. User expectations are satisfied by using
search engines.
Table 1.Foresee Results for the American Customer Satisfaction Index (ACSI) For Search Engines
Search engine 2012 2013 2014 2015 Change in %
for last two
years
GOOGLE 82 77 83 78 -5
BING 81 76 73 72 -1
YAHOO 78 76 71 75 4
MSN 78 74 73 74 1
AOL 74 71 70 74 4
Table 1 makes it clear that there is a change in terms of users’ satisfaction values with reference
to various search engines, including a drop in Google and Bing percentage values. Hence user
interest has to be given due importance since it clearly plays a major role. In the proposed work,
user interest is considered and results provided for the given queries. The proposed page rank
method first analyzes the contents of documents, employs user search queries for the results, and
then optimizes them. Thus the proposed page rank model is a content-based page rank
methodology for search result optimization.
1.2.Overview of Web Mining
A discussion on page ranking presupposes knowledge of web mining, a data mining technique
used to extract information from web documents. The major tasks of web mining are resource
finding, information selection, preprocessing, generalization and analysis. First, data are extracted
from online or offline text data available on the web. The next step is automatic selection and
preprocessing from the retrieved web resources. The third step is an automatic discovery of a
general pattern at individual or multiple sites. Finally, the results are validated and the analysis
arrived at plays a major role in pattern mining. Types of web mining include web content mining,
web structure mining and web usage mining.
1.2.1.Web Content Mining
The discovery of useful information forms web content/data/documents, and the process is also
known as text mining, which is scanning and mining the text, pictures and graphs of a web page
to determine the relevance of the content to a search query, and is related to data mining because
3. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
113
lots of techniques used in data mining are also used in web content mining. It is the process of
retrieving information from the WWW into a more structured form, and provides results lists to
search engines in order of the highest degree of their relevance to the keywords in a query. Also,
it does not provide information about the structure of the content that users are searching for or
the various categories of documents found.
1.2.2.Web Structure Mining
This is the process of discovering a model of the link structure of web pages. For the purpose of
generating data, similarities and relationships are established using hyperlinks. Both page rank
and hyperlink analysis fall into this category, the idea being to generate a structured summary of a
website and a web page. Accordingly, web structure mining can be divided into two kinds to
minimize two chief problems the WWW comes up against as a result of the vast amount of
information at its disposal. The first problem has to do with irrelevant search results, and the next
is the inability to index the vast volume of information provided on the web.
1.2.3.Web Usage Mining
This refers to the automatic discovery and analysis of patterns in a click stream and associated
data collected or generated as a result of user interactions with web resources on one or more
websites. The goal is to capture, model and analyze user behavioral patterns as well as the
profiles of users interacting with the web. The patterns discovered are usually represented as a
collection of pages, objects or resources frequently accessed by a group of users with common
needs or interests. Table 2 represents web mining categories with views of data, main data,
representations and methods of web content mining, web structured mining and web usage
mining.
Table 2. Web Mining Categories
Features
Web Mining
Web Content Mining Web Structured Mining Web Usage Mining
View of Data Structured,
Unstructured
Link Structure Interactivity
Main Data Text document,
Hypertext document
Link Structure Server and Browser
Logs
Representation Collection of words,
phrases, Contents and
relations
Graph Relational Table
Graph
Method Machine Learning
Statistical(including
NLP)
Proprietary algorithms Machine Learning
Statistical and
Association rules
1.3.Literature Review
Search results today are based on web popularity, as a consequence of which a user does not get
the right results. Results that show up on the first page have fewer chances of holding user
interest. Consequently, in order to provide users the needed results first, a new concept of page
ranking was developed to re-rank the results of users’ interests. Agichetein et al. [1] proposed a
new concept for ranking by incorporating user behavior information. They used the Lucene
search API for processing hits made by users, and keywords are mapped into the database thereby
calculating the popularity score, ensuring that peak results come first. The drawback with this
4. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
114
method is that while the results do not change for every user, the need of each user is not the
same. Therefore, some users find good results while others do not. Ahmadi- Abkenari et al [2]
proposed a method in which log ranks are used to calculate website significance values. The
drawback here is that the log data may be old, but since the results clicked change over time they
are unlikely to be good for current users. Anivban kundu[3] in his work two database is used ie.
Global, local and ranking procedures, implemented in a hierarchical fashion. Here, inbound and
outbound web page sessions for matched URLs, and the number of HITS for matched URLs, are
calculated. A major problem, however, is that it is time-consuming. Anuradha et.al [4] proposed
ANT Ranking, which is the ANT colony algorithm for ranking web pages. Here, too, the value of
page ranking is calculated using clicks on the web. User interest on a web page changes
dynamically if the user has visited the page. But, with this method, only those pages a user is
interested in are considered for the purpose of page ranking. The base of page ranking was
proposed by Brin and Page [5], where each web keyword search is used for page ranking and the
process extended by counting links from all pages. The problem is that they do not develop or
model a t for tracing user behaviour. Gomez-Nieto et. al [7], in their similarity processing,
snippet-based visualization of web search results, considered only web snippets to process page
ranking Liao et al [14] used user search behaviour to rank pages. They insist that a task trail
performs better than sessions and a query trail in determining user satisfaction. Also, it increases
users’ web page utility compared to the query trail of other sessions. Shalya Nidhi et al. [17] state
that an effective content-based web page ranking is possible when both web structure and content
mining are mixed in order to get the relevant web page. Here, web structure mining is used to get
the link structure and web content is used to get the content of the web page so that the relevant
page can be fetched as per a user’s query. In our proposed work too, we intend to use both web
structure and content mining.
1.4.Motivation and Justification
The problem with ranking is the need to display a results list, based on user requests or
preferences. In a traditional approach, a search engine always returns the same rank for the same
query submitted at different times or by different users. As massive volumes of data are available,
the principal problem is the need to scan page after page to find the desired information a user
needs. In current search engines, the difficulty is with ordering the search results and then
presenting the most relevant page to the user first. Motivated by this fact, a page ranking
algorithm is proposed in this paper by considering both content and user click-based results. It is
expected that users get effective and relevant results for a given query at a faster rate with page
ranking, Justified by these facts, page ranking is done, taking into consideration each individual’s
keywords, URLS and the keyword-to-URLs. In this way, the user can browse one group of web
pages or another and search for needed results faster.
1.5.Organization of the paper
The rest of the paper is organized as follows. Section 2 describes different page ranking
algorithms. Section 3 discusses the proposed technique. In Section 4, quantitative analysis of page
ranking algorithms are discussed. In Section 5, the results are analyzed and discussed. Section 6
focuses on the conclusion of the research.
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2.PAGE RANKING ALGORITHMS
The three major algorithms for ranking pages i.e. page ranking, weighted page ranking and HITS
(Hyperlink-Induced Topic Search) are presented below.
2.1.Page Ranking
Page ranking, developed by Brin and Page[5] at Stanford University, is used by Google to
calculate the relative importance of web pages. In the beginning, a citation analysis was used. But
the problem was that incoming links that were treated as citations could not provide good results
so Page et al. proposed a new technique in which the page rank value of a web page is computed
by simply counting the number of pages linked to it. These links are called back links.
Figure 1. An example of Back links
In Figure 1, page A is a backlink of page B and page C, while page B and page C are backlinks of
page D. If a backlink comes from an important page, then that link is given higher weightage. The
link from one page to another is considered a vote. The formula they proposed for calculating the
page rank is
PR(A)=(1-d)+d(PR(T1)/C(T1)+…+PR(Tn)/C(Tn)) 1
where PR(Ti) is the page rank of the page Ti which links to page A. C(Ti) is the number of links
put on page Ti. D is the damping factor which is usually set to 0.5, and is used to avoid other
pages having too much influence.
2.2.Weighted Page Rank
This algorithm, proposed by Wenpu Xing and Ali Ghorbani, is an extension of the page ranking
algorithm [7]. It assigns larger rank values to more popular pages instead of dividing the rank
value of a page evenly among its outlink pages. Each outlink page gets a value proportional to its
popularity, with popularity from the number of inlinks and outlinks recorded as Win
(v, u) and
Wout
(v, u) respectively. Win
(v, u) in the equation below is the weight of link (v, u), used to make
calculations based on the number of inlinks of page u and the number of inlinks of all the
reference pages of page v.
Win
(v, u)=
ூೠ
∑ ∈ோ(௩)ூ
2
where Iu and Ip represent the number of links of page u and page p. R(v) denotes the reference
page list of page v, and Wout
(v, u) in the equation is the weight of link (v, u), calculated based on
the number of outlinks of page u and the number of outlinks of all the reference pages of page v
A
C
B
D
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116
Wout
(v, u)=
ைೠ
∑ ∈ோ(௩)ை
3
where Ou and Op represent the number of outlinks of page u and page p. Based on the weight of
inlinks and outlinks, the page ranking formula is modified as
PR(u)=(1-d)+d∑ ܴܲ(ܹ)ݒ(,ݒ ܹ)ݑ௨௧
௩∈(ೠ)
(,ݒ )ݑ 4
2.3.HITS (Hyperlink-Induced Topic Search)
HITS ranks web pages by means of analysis inlinks and outlinks, and was proposed by Klien
Berg[11]. In this algorithm, web pages pointed to by many hyperlinks are called authorities and
the points to many hyperlinks are called hubs. A web page may be a good hub and a good
authority at the same time. Here the WWW is treated as a directed graph G (V,E), where V is a
set of vertices representing pages and E is a set of edges corresponding to links. Figure 2. shows
the hubs and authorities in web. The two methods involved here are sampling and iterative..
.
Figure 2. Hubs and Authorities
In the sampling method, a set of relevant pages for a query are collected. In the iterative method,
hubs and authorities are found using the output of sampling. For calculating the weight of hubs
(Hp) and the weight of authorities (Ap)
Hp=∑ ܣ∈ூ() 5
Ap=∑ ܪ∈() 6
Here Hq is the hub score of a page, Aq is the authority score of a page, I(p) is the set of reference
pages of page p, and B(p) is the set of reference pages of page p. The authority weight of a page
is proportional to the sum of authority weight of the pages that it links to. Problems with HITS
include the fact that hubs and authorities are not easily distinguished and fail to produce relevant
results to user queries because of their equivalent weights. Finally, it is not efficient in real time
and is therefore not implemented in a real-time search engine.
Hubs Authorities
7. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
117
3.PROPOSED PAGE RANKING ALGORITHM
Figure 3. represents the overall working of the proposed page ranking algorithm using a content
and user click-based method. To evaluate a page rank for the web, a user click and content-based
page rank algorithm is proposed. Content analysis is evaluated based on the available content on a
webpage. The entire page rank model can be simulated using three key steps: first, a user query
interface by which a user sends a request for a query. The extracted results are pre-processed
using Porter’s stemming algorithm, and user-clicked contents are traced using user clicks through
a collector. The contents of user-needed data are extracted, analyzed and similarities measured
using the cosine formula. Then the listed results are again rearranged using a ranking based on
content and user clicks and, finally, the listed results are re-ranked and their performance
evaluated using precision, recall, fallout and f-measure.
Figure 3. Block Diagram for the Proposed Page Ranking Algorithm
3.1.Query interface
This can be a graphical user-interface design for providing a system inputs. Inputs can be
search terms required to search for data extraction from data sources.
Performance evaluation
using precision, recall,
fallout and f-measure
User
Query
Search Engine
Query Pre-processing
Content extraction
Content based similarity
User click through Collector
Page ranking based on
content and user click
Re-ranked results
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3.2.Pre-processing
Data in the real world is dirty and incomplete, lacking both attribute values as well as certain
attributes of interest, or containing only noisy and inconsistent aggregate data. There is no quality
in data-extracted results and no quality mining. Further, the queries asked by users are
particularly short. After retrieving the results for a user’s query, the snippets are mixed with
unwanted content and are pre-processed using information-retrieval techniques. The aim is to
generate highly relevant results for a given search query, which can be achieved by stemming and
stop word removal.
3.2.1.Stemming
Stemming is a term used in the information-retrieval process for reducing inflected words to their
word stem or base. Stemming algorithms are used to transform words in a text and improve the
information-retrieval system. The goal is to obtain a one-word description of similar - but not
identical - words. The word obtained in the end has neither meaning nor is grammatically correct,
but it contains a description of and bears a similarity to all the other words it represents. For
example: “implementing” and “implemented” are described by the word “implement.”
3.2.2.Stop Word Elimination
After stemming, it is necessary to remove unwanted words. Stop words are words which are
filtered before or after processing data. A stop word is a word that does not have a meaning, so
eliminating stop words offers better results in a phrase search. In all languages, certain words are
considered stop words, of which there are more than 500 types. For example, words such as “on,”
“and,” “the” and “in,” among others, do not provide useful information. After pre-processing
these snippets, the results are considered for further processing.
3.3.Web Page Content Retrieval
In this phase after the removal of unwanted data, the contents that are frequently occurred
in the snippets are extracted and the relationship between the extracted words is analyzed
here. Also the user clicked contents are collected in the user click through collector.
3.3.1.Extracting Content from Web Snippets
Content from the refined results is extracted by finding frequent item sets in data mining.
When a user types a query, a set of relevant web snippets are returned and if a keyword
or phrase exists frequently in web snippets relating to a particular query, it represents
important content related to the query because it exists with the query in the top
documents. To measure interest in a particular keyword or phrase ki extracted from web
snippets:
support(k୧) =
ୱ(୩)
୬
. |k୧| 7
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119
Table 3 Frequent Words Extracted for The Query “Web Mining”
Word Support
Data 0.5
Techniques 0.108696
Patterns 0.130435
Analysis 0.108696
Information 0.173913
Knowledge 0.108696
Usage 0.217391
where sf(ki) is the snippet frequency of the keyword or phrase ki , n is the number of web
snippets returned, and |ki| is the number of terms in the keyword or phrase ki If the
support of a keyword or phrase ki is greater than the threshold s, then ki acts as a concept
for the query q. Table 3 shows a sample of frequent words extracted for the query “web
mining. The maximum length of a concept is limited. This process not only reduces
processing time but also avoids extraction of meaningless content.
3.3.2.Content-Based Similarity
In extracting content-based similarity, a signal-to-noise formula is used to establish the
similarity between keywords k1 and k2. The two keywords from a query q are similar if
they coexist frequently in web snippets arising from the query q.
sim(kଵ, kଶ) =
୬∗ୢ(୩భ୩మ)
ୢ(୩భ)∗ୢ(୩మ)
/ log n 8
where n is the number of documents in mass, df(k) is the document frequency of the keyword k,
and ݂݀(݇ଵܷ݇ଶ) is the joint document frequency of k1 and k2. The similarity sim(k1,k2) obtained
using the above formula always lies between [0, 1]. In search engine contexts, two concepts ki
and kj can coexist in web snippets
݉݅ݏோ,௦௧൫݇, ݇൯ = ݈݃
∗௦ೞ൫ ∪ ೕ൯
௦ೞ()∗௦ೞ൫ೕ൯
/ log ݊ 9
Table 4 Relationship between the Words Extracted For The Query “Web Mining”
ID Concept1 Concept2 Relations
1 Data Techniques 0.108695652174
2 Data Patterns 0.0652173913043
3 Data Analysis 0.0869565217391
4 Data Information 0.0869565217391
5 Data Knowledge 0.108695652174
6 Data Usage 0.0652173913043
7 Techniques Data 0.108695652174
8 Techniques Patterns 0.0434782608696
9 Techniques Analysis 0.0217391304348
10 Techniques Information 0.0217391304348
11 Techniques Knowledge 0.0217391304348
10. International Journal on Computational Science & Applications (IJCSA) Vol.5,No.6, December 2015
120
12 Techniques Usage 0
13 Patterns Data 0.0652173913043
14 Patterns Techniques 0.0434782608696
15 Patterns Analysis 0.0217391304348
16 Patterns Information 0
17 Patterns Knowledge 0
18 Patterns Usage 0.0652173913043
19 Analysis Data 0.0869565217391
20 Analysis Techniques 0.0217391304348
21 Analysis Patterns 0.0217391304348
22 Analysis Information 0.0217391304348
23 Analysis Knowledge 0.0652173913043
24 Analysis Usage 0.0434782608696
where ݂ݏ௦௧൫݇ ∪ ݇൯/ ݂ݏ௦௧൫݇ ∪ ݇൯ are joint snippet frequencies of the
concept ki and kj in web snippets. ݂ݏ௦௧(݇). ݂ݏ௦௧൫݇൯ is the snippet frequency of
the concepts ki and kj respectively for finding essential features from data word frequency
using the following formula
݀ݎݓ ݂ݕܿ݊݁ݑݍ݁ݎ =
. ௧௦ ௪ௗ ௗ௨௧
௧௧ ௪ௗ௦ ௗ௨௧
10
Now, using Euclidean distance, the similarity between the extracted data and the available set of
data is computed and, according to the distance obtained, the data is placed in a similar set where
it actually belongs. Table 4 is the relationships between the words extracted for the query “Web
Mining”. In this, the relationships made between the frequently extracted concepts are evaluated.
3.3.3.User Click-through Collectors
The relationship that exists between concepts is processed by considering a user’s click-through.
User-clicked queries are called user-positive preferences and others are user-negative preferences.
When a user clicks on a query, the weight of the extracted concept is incremented by 1 to show
user interest. Other concepts related to the user’s query are also incremented to a similar score. If
the concept is closely related to the user’s positive result, then it is incremented to a higher value.
Otherwise, it is incremented to a small fraction close to zero, by means of which a user log is
created. After finding the data needed using the search engine, it is ranked according to its
relevance to the user’s query, requiring that a new kind of system be implemented a consequence.
Thus the proposed work is designed in the manner set forth below. The given ranking system is
implemented in components of different text processing and weight estimation techniques.
3.4.Page Ranking Based on Content and User Click
User-interested results for a query are stored in a database and, over time, collected using user
clicks through collector. This is termed a query log and provides useful information about
searchers' queries and what users are interested in. A problem peculiar to a query log is that it has
no relational information other than a query and a click. Considerable portions of queries are rare,
with few clicks or even no clicks at all for certain queries [20]. In the proposed work, a combined
user profile method is applied to Google search results and the retrieved results are re-ranked,
based on user-interested results with level re-ranking being used for this particular group. Both
web structure and web content mining are used to get users the relevant results anticipated. Web
content mining is used here to get the linking structure of a web page and trace the content and
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similarity between each item of the contents of the said web page. Initially, a user visits a web
page at random but this change over time. Here, user interest is calculated using clicks on web
links. The quality of user interest changes dynamically with the number of user visits to the web
page. The probability Pi of a user-taken decision equals user interest relative to the sum of all
user-interested values. To find the probability of a user choosing a web page ‘i’ is
Pi=
∑ సೄ
11
Here ui is the user interest and S is the similarity of contents in the web page.
As user interest changes, accordingly user interest value ui is updated, along with the time spent
by the user when the page was visited for the query. As a result, the relevance of the page to the
user increases greatly and the probability of its being chosen also increases correspondingly.
When the user visits the page, the quantum of user interest is updated. The volume of the web
page increases, proportional to its quality.
ܷ
௧ାଵ
=ܷ
௧
+ ∆ܷ
௧
12
where ui user interested value at time in sec and and ∆ܷ
௧
the amount of user interest saved at
time t left by the user. It can be changed, depending on user interest in terms of clicks.
4.QUANTITATIVE ANALYSIS OF PAGE RANKINGALGORITHMS
Different page ranking algorithms are discussed and quantitative differences between each
represented in Table 5. The mining technique used, input parameters, time complexity and
limitations are discussed.
Table 5 Quantitative Analysis Of Different Pager Ranking Algorithms
Quantitative
Parameters
Different Pager Ranking Algorithms
Page Rank Weighted Page Rank HITS Proposed
Page
Ranking
Mining
technique
used
WSM WSM WSM and WCM WSM and
WCM
I/P
Parameters
Back links Front and Back links Front, Back links
and content
Front, Back
link, content
and user click
Complexity O(log N) <O(log N) <O(log N) ≤O(log N)
Limitation Query
independent
Query Independent Topic drift and
efficiency
problem
More
computation
time
Working Results are
stored according
to importance of
pages
Results are stored
according to
importance of pages
Compute hub and
authority scores
of highly relevant
pages
Consider the
user clicked
links(VOL),
content of the
snippets and
user relevant
pages are
stored
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From the table, it is clear that a lot of new ideas have been implemented to provide good results in
content and user click-based page ranking, which are proved by providing experimental results
like the relevancy rule, precision, recall, fall out and f-measure
5.EXPERIMENTAL RESULTS AND DISCUSSION
5.1. Performance Metrics
The proposed method is compared with existing methods using performance measures like
precision, recall, fallout and f-measure. They are computed as below:
5.1.1. Precision
Precision is the fraction of documents retrieved that are relevant to a user’s information needs. It
takes all retrieved documents into account.
Precision=
௩௧ ௗ௨௧ ∩ ௧௩ௗ ௗ௨௧௦
௧௩ௗ ௗ௨௧௦
13
5.1.2.Recall
Recall is the fraction of documents successfully retrieved and relevant to a query. Also called
sensitivity, it can be looked at as the probability that a relevant document is retrieved by the
query.
Recall=
௩௧ ௗ௨௧ ∩௧௩ௗ ௗ௨௧௦
௩௧ ௗ௨௧௦
14
It is easy to achieve a recall of 100 percent by retrieving all documents in response to a query.
Hence recall alone is not enough, and the number of non-relevant documents is required to be
measured as well.
5.1.3.Fallout
Fallout is the proportion of non-relevant documents retrieved from all the non-relevant documents
available. It can be looked at as the probability that a non-relevant document is retrieved by a
query.
Fallout=
௩௧ ௗ௨௧௦ ∩ ௩௧ ௗ௨௧௦
௩௧ ௗ௨௧௦
15
It is easy to achieve fallout of 0 percent by returning zero documents in response to a query.
5.1.4. F-measure
F-measure is the harmonic mean of precision and recall, and provides good results when
precision and recall provide good results.
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F-measure= 2.
௦∗
௦ା
16
5.1.5.Relevancy Rule
The relevancy of a page for a given query depends on the category and position the needed data
holds in a page list. The larger the relevancy values, the better the results. Relevancy is calculated
using the formula
K=∑ (݊ − ݅) ∗ ܹ∈ோ()
17
where i denotes the i page in the result page-list R(p), n represents the first n pages chosen from
the list R(p), and Wi is the weight of page I with four categories of very relevant pages (VRP),
relevant pages (RP), weakly relevant pages (WRP) and irrelevant pages (IR).
5.2.Dataset
This section provides a performance analysis of the proposed technique, and a comparative study
is also provided with the traditional method of page rank estimation. The Bing API is used for
preparing a dataset of user queries, with Bing search results for 30 days in June 2015 being
considered. Default snippet counts are set to 100. User-clicked contents, as well as users’ positive
and negative preferences are collected and re-ranked based on content and user-clicked data, along
with bookmarked contents from user log files. For evaluation some of the queries used may be
ambiguous, entity names and general terms and are shown below. Table 7 shows the queries used
for evaluation of search results.
Table 7 Queries Used For Evaluation
Types Queries
Ambiguous Apple ,Tiger, Penguin, Jaguar
Entity names Dell Disney, Raja
General terms Sun , Music, Network
5.3.Experimental Results
Experiments are conducted using different queries to check the performance of the retrieved
results based on the metrics like precision, recall, fallout, f-measure and relevancy rule and are
shown from Table 8 to Table 11 respectively. Precision values vary in accordance with changes
in user interest. The given precision values define the relevancy of search results obtained during
experimentation. Search recall values, which are measurements of accuracy, are measured in this
section. Fallout values for the queries are evaluated and the values which are less are optimal
because here the error rate is considered. F-measure is calculated by considering the precision and
recall values estimated and the results are listed below.
Table 8 Precision Values for Different Page Ranking Algorithms
Queries Precision Values For Different Page Ranking Algorithms
PR WPR HITS Proposed Page Ranking
Apple 0.862 0.904 0.961 0.991
Data mining 0.886 0.870 0.952 0.926
PHP 0.799 0.893 0.904 0.919
Web 0.589 0.791 0.842 0.993
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Jaguar 0.647 0.751 0.835 0.893
Google 0.798 0.812 0.847 0.885
Network 0.719 0.729 0.787 0.945
Tiger 0.731 0.771 0.753 0.932
Table 9 Recall Values for Different Page Ranking Algorithms
Queries Recall Values For Different Page Ranking Algorithms
PR WPR HITS Proposed Page Ranking
Apple 0.977 0.979 0.970 0.989
Data mining 0.993 0.937 0.950 0.96
PHP 0.968 0.943 0.971 0.979
Web 0.974 0.987 0.958 0.98
Jaguar 0.978 0.876 0.984 0.99
Google 0.941 0.969 0.940 1.0
Network 0.969 0.947 0.947 1.0
Tiger 0.945 0.940 0.945 0.952
Table 10 Fallout Values for Different Page Ranking Algorithms
Queries Fallout Values for Different Page Ranking Algorithms
PR WPR HITS Proposed Page Ranking
Apple 0.023 0.021 0.030 0.011
Data mining 0.007 0.063 0.050 0.04
PHP 0..032 0.057 0.029 0.021
Web 0.026 0.013 0.042 0.02
Jaguar 0.022 0.124 0.016 0.01
Google 0.059 0.031 0.060 0.00
Network 0.031 0.053 0.053 0.00
Tiger 0.055 0.060 0.055 0.048
Table 11 F-Measure Values for Different Page Ranking Algorithms
Queries F-Measure Values for Different Page Ranking Algorithms
PR WPR HITS Proposed Page Ranking
Apple 0.938 0.917 0.962 0.99
Data mining 0.926 0.910 0.951 0.943
PHP 0.869 0.864 0.895 0.947
Web 0.847 0.737 0.895 0.986
Jaguar 0.849 0.743 0.902 0.938
Google 0.872 0.875 0.890 0.938
Network 0.831 0.816 0.859 0.991
Tiger 0.848 0.822 0.837 0.941
According to the results obtained in table 8, the performance of the proposed technique is
optimal, when compared to other, traditional page ranking approaches. It is easy to achieve a
recall of 100 percent by retrieving all documents in response to a query. Hence recall alone is not
enough, and the number of non-relevant documents is required to be measured as well. Hence
fallout is considered and from the table 10, that the proposed page ranking has fewer non -
relevant documents retrieved than other page ranking algorithms. Table 11 makes it clear that the
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proposed method provides good results compared to other existing methods because precision
and recall for the existing methods are fewer, compared to the proposed method. Hence the f-
measure value for content and user click-based page ranking gives good results.
Relevant pages retrieved for different queries are calculated by varying the number of snippets by
50,100 and 150 and relevancy values measured. Values are calculated for different page ranking
algorithms like proposed page ranking, weighted page ranking (WPR), page ranking (PR) and
Hyperlink Induced Topic Search (HITS).
-
Figure 4. Comparison of Different Page Ranking Algorithms Based On Relevancy
From Figure 4 it is inferred that the relevancy rate of proposed page ranking is comparatively
higher than page ranking algorithms like PR, WPR, and HITS across various numbers of
snippets. It can also be inferred that the relevance rate for WPR is comparatively better than that
of other page ranking algorithms.
6.CONCLUSIONS
Page ranking is essential to ascertain the importance of a web page for a given search query and
rank the results according to their relevance. In this study, different page ranking techniques are
investigated and a new kind of page rank algorithm proposed and designed. This page rank
algorithm provides a rank according to the relevance of a user’s query and the contents available
in web pages. In addition, the relevancy of search results is measured in terms of precision, recall
and F-measure. These results demonstrate the efficacy of relevant ranks for the search results
available. The proposed work is intended to provide an efficient page rank technique using an
analysis of web page content. The page rank technique presented ranks results according to the
importance of a web page, user search query and the content available in the web page. The
proposed technique is efficient but not generalized, providing efficient, scenario-specific page
ranking. Therefore, in the near future, the proposed technique is to be extended to derive a
generalized framework for page rank estimation.
0
5
10
15
20
25
30
35
40
50 100 150
Relevancy
No. Of snippets
Comparision of page ranking based on relevancy
PR
WPR
HITS
Proposed Page Ranking
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Authors
Dhiliphan Rajkumarhas a strong passion in Web Mining, Pattern recognition and Social
networking. He is currently pursuing Ph.D degree in Computer Engineering,
ManonmaniamSundaranar University, India.
Dr. A. Suruliandi received his B.E. (1987) in Electronics and Communication
Engineering from Coimbatore Institute of Technology, Coimbatore, Bharathiyar
University, Tamilnadu, India. He received M.E. (2000) in Computer Science and
Engineering from Government College of Engineering Tirunelveli. He also received
Ph.D. in Computer Science (2009) from Manonmaniam Sundaranar University as
well.He is having more than 27 years of teaching experience. He is having more than 80
publications in International journals and conferences. His research interests include Pattern recognition,
Image processing, Remote sensing and Texture analysis.
Selvaperumal has a strong passion in Web Mining, Data Mining, Machine learning, NLP
and Art ificial Intelligence. He is currently pursuing Ph.D degree in Computer
Engineering, ManonmaniamSundaranar University, India.