1) The document proposes enhancing the Weighted PageRank algorithm by incorporating visits of links (VOL) to calculate page rank. It takes into account both the number of visits of inlinks and outlinks of pages.
2) A new algorithm, called Enhanced Weighted PageRank using VOL (EWPR VOL), is presented. It calculates page popularity based on the number of visits of inlinks (WVOLin) and outlinks (WVOLout).
3) The EWPR VOL algorithm is demonstrated using a sample web graph to calculate page rank values for pages A, B and C based on the number of visits of their inlinks and outlinks.
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
A Generalization of the PageRank Algorithm : NOTESSubhajit Sahu
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?).
---
Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network.
Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
PageRank is the algorithm used by Google to rank web pages for search results. It analyzes the link structure of the web by treating inbound links as votes and ranking pages based on the number and quality of votes they receive from other pages. PageRank relies on the democratic nature of the web and its link structure as an indicator of a page's importance. It models the behavior of a random web surfer who gets bored and jumps to random pages. Google calculates PageRank values for billions of web pages to determine their relative importance and relevance to search queries in a matter of hours. Beyond search, PageRank has applications for reputation systems, collaborative filtering, opinion polls, and analyzing other real-world networks.
PageRank is an algorithm used by the Google web search engine to rank websites in the search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages.
This document provides an overview of the PageRank algorithm. It begins with background on PageRank and its development by Brin and Page. It then introduces the concepts behind PageRank, including how it uses the link structure of webpages to determine importance. The core PageRank algorithm is explained, modeling the web as a graph and calculating page importance based on both the number and quality of inbound links. Iterative methods like power iteration are described for approximating solutions. Examples are given to illustrate PageRank calculations over multiple iterations. Implementation details, applications, advantages/disadvantages are also discussed at a high level. Pseudocode is included.
PageRank is an algorithm created by Larry Page and Sergey Brin that ranks web pages based on the number and quality of links to a page. It interprets a link from page A to page B as a vote for page B. PageRank is calculated through an iterative process where each page is given an initial ranking that is then recalculated based on the rankings of pages that link to it. The damping factor determines how much a page's ranking is passed on through its outbound links. A higher damping factor results in more equal distribution of ranking across all pages on a site.
The document discusses how Markov chains are used as the methodology behind PageRank to rank web pages on the internet. It provides an overview of key concepts, including defining Markov chains and stochastic processes. It explains the idea behind PageRank, treating each web page as a journal and measuring importance based on the number of citations/links to other pages. The PageRank algorithm models web surfing as a Markov chain and the steady-state probabilities of the chain indicate the importance of each page.
1. The document discusses different levels of link analysis on the web, including macroscopic, microscopic, and mesoscopic views.
2. It presents methods for calculating PageRank and functional rankings through various damping functions like exponential and linear damping.
3. The recursive formulation of linear damping is also described to allow computation without storing the full link matrix in memory.
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
A Generalization of the PageRank Algorithm : NOTESSubhajit Sahu
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?).
---
Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network.
Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
PageRank is the algorithm used by Google to rank web pages for search results. It analyzes the link structure of the web by treating inbound links as votes and ranking pages based on the number and quality of votes they receive from other pages. PageRank relies on the democratic nature of the web and its link structure as an indicator of a page's importance. It models the behavior of a random web surfer who gets bored and jumps to random pages. Google calculates PageRank values for billions of web pages to determine their relative importance and relevance to search queries in a matter of hours. Beyond search, PageRank has applications for reputation systems, collaborative filtering, opinion polls, and analyzing other real-world networks.
PageRank is an algorithm used by the Google web search engine to rank websites in the search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages.
This document provides an overview of the PageRank algorithm. It begins with background on PageRank and its development by Brin and Page. It then introduces the concepts behind PageRank, including how it uses the link structure of webpages to determine importance. The core PageRank algorithm is explained, modeling the web as a graph and calculating page importance based on both the number and quality of inbound links. Iterative methods like power iteration are described for approximating solutions. Examples are given to illustrate PageRank calculations over multiple iterations. Implementation details, applications, advantages/disadvantages are also discussed at a high level. Pseudocode is included.
PageRank is an algorithm created by Larry Page and Sergey Brin that ranks web pages based on the number and quality of links to a page. It interprets a link from page A to page B as a vote for page B. PageRank is calculated through an iterative process where each page is given an initial ranking that is then recalculated based on the rankings of pages that link to it. The damping factor determines how much a page's ranking is passed on through its outbound links. A higher damping factor results in more equal distribution of ranking across all pages on a site.
The document discusses how Markov chains are used as the methodology behind PageRank to rank web pages on the internet. It provides an overview of key concepts, including defining Markov chains and stochastic processes. It explains the idea behind PageRank, treating each web page as a journal and measuring importance based on the number of citations/links to other pages. The PageRank algorithm models web surfing as a Markov chain and the steady-state probabilities of the chain indicate the importance of each page.
1. The document discusses different levels of link analysis on the web, including macroscopic, microscopic, and mesoscopic views.
2. It presents methods for calculating PageRank and functional rankings through various damping functions like exponential and linear damping.
3. The recursive formulation of linear damping is also described to allow computation without storing the full link matrix in memory.
ESWC 2013: A Systematic Investigation of Explicit and Implicit Schema Informa...Thomas Gottron
The document presents a method to analyze the redundancy of schema information on the Linked Open Data cloud. It examines the entropy and conditional entropy of type and property distributions across several LOD datasets. The results show that properties provide more informative schema information than types, and indicate types better than types indicate properties. There is generally high redundancy between types and properties, ranging from 63-88% on the analyzed segments of the LOD cloud. Future work could analyze schema information at the data provider level and over time.
Use Email Marketing wisely; Stand out from Junk Mailbelieve52
Email marketing is an effective but cheap way to promote products, however junk mail is common so marketers must stand out. The document provides tips for effective email marketing: 1) use text rather than images to avoid being marked as spam, 2) avoid attachments which can spread viruses, 3) avoid fancy HTML which may not display properly on all email clients, 4) address the recipient by name to increase interest, and 5) provide a valid sender address to avoid being blocked or marked as spam.
Secured E-Learning Content on USB/HDD DeviceIOSR Journals
This document summarizes a system for securing e-learning content stored on USB/HDD devices. The system provides three-level authentication of the user, USB device, and user's system. It encrypts the course content using the unique hardware details of the USB and system. Only an authenticated user on their authenticated system with the authenticated USB can decrypt and access the licensed content. The system aims to prevent piracy of e-learning content while allowing authorized offline access via USB.
1) The document proposes a more secure implementation of the AES encryption algorithm by making the S-box structure nonlinear and dynamic.
2) A biometrics scheme is combined with the AES encryption and decryption to improve authentication security. Fingerprints are used in both encryption and decryption processes.
3) The implementation generates a random virtual S-box for each input by XORing the default AES S-box with a derived S-box, making the S-box structure nonlinear and dynamic. This improves security against attacks on the AES algorithm.
An Improvement in Power Management in green Computing using Neural NetworksIOSR Journals
This document summarizes previous work on green computing and power management techniques using neural networks. It proposes a new technique using neural networks and dynamic clustering for energy conservation in green computing. Previous approaches focused on virtualization, power management, material recycling, and algorithms for efficient routing and clustering. The proposed technique would use a neural network's learning capabilities combined with dynamic clustering to improve energy efficiency. It was implemented in a simulation and results were presented graphically. The goal is to reduce resource consumption and electronic waste through more efficient power management.
This document summarizes a research paper that proposes a method to improve web image search results by re-ranking the initial text-based search results using visual similarity measures. The researchers develop an adaptive visual similarity approach that categorizes the query image and then uses a specific similarity measure tailored to each category to re-rank the images based on visual features. They test their method on search results from Google Image Search and Microsoft Live Image Search and find it effectively improves search accuracy by better incorporating visual content into the ranking.
7 Tips for Selling Expensive Collectibles On eBaybelieve52
The document provides 7 tips for selling expensive collectibles on eBay:
1. Set a reserve price that is the minimum you will accept and start the bidding very low to attract buyers.
2. Provide detailed descriptions of how the item will be carefully packed to prevent damage.
3. Require buyers to pay for insurance to protect both parties from liability if the collectible is broken.
4. Verify the authenticity of the collectible by providing details, photos or certificates of authenticity.
5. Do not offer returns but suggest using an escrow service for high value items to assure buyers.
6. Take extra precautions like verifying funds if shipping internationally due to higher fraud risks overseas.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
A Secure Software Implementation of Nonlinear Advanced Encryption StandardIOSR Journals
This document summarizes a research paper on image steganography using a polynomial key for covert communications. It proposes a new steganographic encoding scheme that separates the color channels of bitmap images and hides messages randomly in the least significant bit of one color component where the other two components' colors equal the selected key. The secret message, cover image, and pseudorandom seed generated by a polynomial are inputs. Pixels where the red and green components match the key are identified, and the secret bits are randomly inserted in the blue component's least significant bit using the seed. Statistical analysis found no difference in quality between the original and stego image. The scheme aims to provide a covert communication method using open systems.
Web Mining Research Issues and Future Directions – A SurveyIOSR Journals
This document summarizes research on web mining techniques. It begins with an abstract describing how web mining aims to extract useful information from vast amounts of unstructured web data. It then reviews various web mining techniques including web content mining, web structure mining, and web usage mining. The document surveys literature on pattern extraction techniques such as association rule mining, clustering, classification, and sequential pattern mining. It also discusses challenges in pre-processing web data and issues related to scaling up data mining algorithms for large web datasets. In closing, the document outlines future research directions in web mining including dealing with unstructured data and multimedia content.
This document describes the design and implementation of a numerically controlled oscillator (NCO) on an FPGA. An NCO generates sinusoidal signals through a phase accumulator and lookup table. The authors implemented an NCO with 9-bit phase resolution, 54.18 dB spur level, 24-bit frequency resolution, and output signals of sine and cosine waves. They designed the NCO in VHDL, simulated it in Xilinx ISE, and tested it on a Spartan-2 FPGA board. The hardware results matched the simulation results, demonstrating frequencies from 1.25 MHz to 7.5 MHz with matching output on a spectrum analyzer. The FPGA-based NCO can be used for applications like software defined
Linux-Based Data Acquisition and Processing On Palmtop ComputerIOSR Journals
This document describes the development of a data acquisition and processing system using a palmtop computer running Linux. The system uses a PCMCIA data acquisition card and free Linux drivers and libraries. A demo application was created that can sample 1024 signals from a microphone at 100 ksamples/s and compute the fast Fourier transform of the signal up to 6 times per second. The document outlines the hardware and software implementation including developing the C code on a desktop, cross compiling it for the palmtop, and downloading and testing the executable on the palmtop computer. It provides details on using COMEDI libraries for data acquisition and TCL/Tk for the graphical user interface.
Color to Gray and back’ using normalization of color components with Cosine, ...IOSR Journals
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
A Novel High Order Tree for Securing Key Management for Multicast ServicesIOSR Journals
The document proposes a novel high order tree scheme called MKE-MGKM for securing key management in multiple multicast group environments. The MKE-MGKM scheme uses asymmetric master and slave keys generated from a master key encryption algorithm. This asymmetry allows rekeying overhead to be reduced by modifying only the master key when a slave key is updated, while keeping other slave keys unchanged. Numerical analysis and simulations show the MKE-MGKM scheme can reduce the storage overhead of a key distribution center by 75% and the storage overhead of a user by up to 85%, as well as communication overhead by 60%, compared to existing schemes. The scheme aims to address limitations of existing group key management schemes for multiple co-existing multicast
This document summarizes the Firefly Algorithm, a meta-heuristic optimization algorithm inspired by the flashing behaviors of fireflies. It describes the concepts behind the algorithm, including how attractiveness and light intensity are formulated based on distance. The algorithm is tested on six unconstrained benchmark functions to evaluate its performance. The results show that the Firefly Algorithm finds the global optimum for most test functions and performs better with larger population sizes, though convergence speed decreases with larger populations due to increased computational complexity.
This document summarizes a research paper that aims to detect and prevent wormhole attacks in wireless sensor networks. It first provides background on wormhole attacks, where an attacker tunnels network traffic to another location to compromise routing. It then reviews related work detecting wormholes using cryptography, location verification, or intrusion detection. The paper proposes a system with guard nodes that collaboratively monitor links to detect compromised nodes. It describes modules for network topology establishment, attack establishment through different wormhole modes, and an elimination mechanism where guard nodes isolate attackers once malicious behaviors exceed thresholds. Simulations test the ability of this scheme to improve security against wormhole attacks in resource-constrained wireless sensor networks.
Data Allocation Strategies for Leakage DetectionIOSR Journals
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
A Modular Approach To Intrusion Detection in Homogenous Wireless NetworkIOSR Journals
This document discusses a modular approach to intrusion detection in homogeneous wireless networks. It begins by introducing wireless networks and the need for intrusion detection systems (IDS) due to security vulnerabilities. It then discusses different types of IDS, including signature-based detection that identifies known attacks, and anomaly-based detection that identifies deviations from normal behavior but can result in high false positives. The document proposes a modular approach combining advantages of signature-based and anomaly-based detection for high detection rates and low false positives. Requirements for IDS in wireless networks are also outlined.
Design and Implementation of Single Leg Reduce Switch Count Dual Output Inver...IOSR Journals
This document describes a proposed three-switch single-leg inverter topology that can independently supply two AC loads using reduced semiconductor switches compared to conventional six-switch topologies. The three-switch inverter uses three semiconductor switches and three parallel capacitors to generate independent outputs of varying frequency and amplitude. Simulation and experimental results show that the three-switch inverter can successfully drive two AC loads independently while reducing components, cost, size and weight compared to traditional designs.
The document summarizes a study of the dielectric properties of nano-crystalline Mn-Zn ferrites. Samples of ZnxMn1-xFe2O4 where x ranges from 0.2 to 0.8 were synthesized using a solid-state route and characterized. The ac conductivity σ, dielectric constant ε', dielectric loss ε'', and loss tangent tan δ were measured from 100 Hz to 20 MHz. ε' and ε'' decreased with increasing frequency. σ was nearly frequency independent below 1 MHz and increased sharply above. The maximum dielectric constant and conductivity occurred for x=0.2, attributed to space charge polarization. The conduction mechanism was explained by electron hopping between Fe2+ and Fe
Evaluation of Web Search Engines Based on Ranking of Results and FeaturesWaqas Tariq
Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
ESWC 2013: A Systematic Investigation of Explicit and Implicit Schema Informa...Thomas Gottron
The document presents a method to analyze the redundancy of schema information on the Linked Open Data cloud. It examines the entropy and conditional entropy of type and property distributions across several LOD datasets. The results show that properties provide more informative schema information than types, and indicate types better than types indicate properties. There is generally high redundancy between types and properties, ranging from 63-88% on the analyzed segments of the LOD cloud. Future work could analyze schema information at the data provider level and over time.
Use Email Marketing wisely; Stand out from Junk Mailbelieve52
Email marketing is an effective but cheap way to promote products, however junk mail is common so marketers must stand out. The document provides tips for effective email marketing: 1) use text rather than images to avoid being marked as spam, 2) avoid attachments which can spread viruses, 3) avoid fancy HTML which may not display properly on all email clients, 4) address the recipient by name to increase interest, and 5) provide a valid sender address to avoid being blocked or marked as spam.
Secured E-Learning Content on USB/HDD DeviceIOSR Journals
This document summarizes a system for securing e-learning content stored on USB/HDD devices. The system provides three-level authentication of the user, USB device, and user's system. It encrypts the course content using the unique hardware details of the USB and system. Only an authenticated user on their authenticated system with the authenticated USB can decrypt and access the licensed content. The system aims to prevent piracy of e-learning content while allowing authorized offline access via USB.
1) The document proposes a more secure implementation of the AES encryption algorithm by making the S-box structure nonlinear and dynamic.
2) A biometrics scheme is combined with the AES encryption and decryption to improve authentication security. Fingerprints are used in both encryption and decryption processes.
3) The implementation generates a random virtual S-box for each input by XORing the default AES S-box with a derived S-box, making the S-box structure nonlinear and dynamic. This improves security against attacks on the AES algorithm.
An Improvement in Power Management in green Computing using Neural NetworksIOSR Journals
This document summarizes previous work on green computing and power management techniques using neural networks. It proposes a new technique using neural networks and dynamic clustering for energy conservation in green computing. Previous approaches focused on virtualization, power management, material recycling, and algorithms for efficient routing and clustering. The proposed technique would use a neural network's learning capabilities combined with dynamic clustering to improve energy efficiency. It was implemented in a simulation and results were presented graphically. The goal is to reduce resource consumption and electronic waste through more efficient power management.
This document summarizes a research paper that proposes a method to improve web image search results by re-ranking the initial text-based search results using visual similarity measures. The researchers develop an adaptive visual similarity approach that categorizes the query image and then uses a specific similarity measure tailored to each category to re-rank the images based on visual features. They test their method on search results from Google Image Search and Microsoft Live Image Search and find it effectively improves search accuracy by better incorporating visual content into the ranking.
7 Tips for Selling Expensive Collectibles On eBaybelieve52
The document provides 7 tips for selling expensive collectibles on eBay:
1. Set a reserve price that is the minimum you will accept and start the bidding very low to attract buyers.
2. Provide detailed descriptions of how the item will be carefully packed to prevent damage.
3. Require buyers to pay for insurance to protect both parties from liability if the collectible is broken.
4. Verify the authenticity of the collectible by providing details, photos or certificates of authenticity.
5. Do not offer returns but suggest using an escrow service for high value items to assure buyers.
6. Take extra precautions like verifying funds if shipping internationally due to higher fraud risks overseas.
This document presents a method for detecting cancer in Pap smear cytological images using bag of texture features. The method involves segmenting the nucleus region from the images, extracting texture features from blocks within the nucleus region, clustering the features to build a visual dictionary, and representing each image as a histogram of visual words present. The histograms are then used to retrieve similar images from a database using histogram intersection as the distance measure. Experiments were conducted using different block sizes and number of clusters, achieving up to 90% accuracy in identifying cancerous versus normal cells.
A Secure Software Implementation of Nonlinear Advanced Encryption StandardIOSR Journals
This document summarizes a research paper on image steganography using a polynomial key for covert communications. It proposes a new steganographic encoding scheme that separates the color channels of bitmap images and hides messages randomly in the least significant bit of one color component where the other two components' colors equal the selected key. The secret message, cover image, and pseudorandom seed generated by a polynomial are inputs. Pixels where the red and green components match the key are identified, and the secret bits are randomly inserted in the blue component's least significant bit using the seed. Statistical analysis found no difference in quality between the original and stego image. The scheme aims to provide a covert communication method using open systems.
Web Mining Research Issues and Future Directions – A SurveyIOSR Journals
This document summarizes research on web mining techniques. It begins with an abstract describing how web mining aims to extract useful information from vast amounts of unstructured web data. It then reviews various web mining techniques including web content mining, web structure mining, and web usage mining. The document surveys literature on pattern extraction techniques such as association rule mining, clustering, classification, and sequential pattern mining. It also discusses challenges in pre-processing web data and issues related to scaling up data mining algorithms for large web datasets. In closing, the document outlines future research directions in web mining including dealing with unstructured data and multimedia content.
This document describes the design and implementation of a numerically controlled oscillator (NCO) on an FPGA. An NCO generates sinusoidal signals through a phase accumulator and lookup table. The authors implemented an NCO with 9-bit phase resolution, 54.18 dB spur level, 24-bit frequency resolution, and output signals of sine and cosine waves. They designed the NCO in VHDL, simulated it in Xilinx ISE, and tested it on a Spartan-2 FPGA board. The hardware results matched the simulation results, demonstrating frequencies from 1.25 MHz to 7.5 MHz with matching output on a spectrum analyzer. The FPGA-based NCO can be used for applications like software defined
Linux-Based Data Acquisition and Processing On Palmtop ComputerIOSR Journals
This document describes the development of a data acquisition and processing system using a palmtop computer running Linux. The system uses a PCMCIA data acquisition card and free Linux drivers and libraries. A demo application was created that can sample 1024 signals from a microphone at 100 ksamples/s and compute the fast Fourier transform of the signal up to 6 times per second. The document outlines the hardware and software implementation including developing the C code on a desktop, cross compiling it for the palmtop, and downloading and testing the executable on the palmtop computer. It provides details on using COMEDI libraries for data acquisition and TCL/Tk for the graphical user interface.
Color to Gray and back’ using normalization of color components with Cosine, ...IOSR Journals
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
A Novel High Order Tree for Securing Key Management for Multicast ServicesIOSR Journals
The document proposes a novel high order tree scheme called MKE-MGKM for securing key management in multiple multicast group environments. The MKE-MGKM scheme uses asymmetric master and slave keys generated from a master key encryption algorithm. This asymmetry allows rekeying overhead to be reduced by modifying only the master key when a slave key is updated, while keeping other slave keys unchanged. Numerical analysis and simulations show the MKE-MGKM scheme can reduce the storage overhead of a key distribution center by 75% and the storage overhead of a user by up to 85%, as well as communication overhead by 60%, compared to existing schemes. The scheme aims to address limitations of existing group key management schemes for multiple co-existing multicast
This document summarizes the Firefly Algorithm, a meta-heuristic optimization algorithm inspired by the flashing behaviors of fireflies. It describes the concepts behind the algorithm, including how attractiveness and light intensity are formulated based on distance. The algorithm is tested on six unconstrained benchmark functions to evaluate its performance. The results show that the Firefly Algorithm finds the global optimum for most test functions and performs better with larger population sizes, though convergence speed decreases with larger populations due to increased computational complexity.
This document summarizes a research paper that aims to detect and prevent wormhole attacks in wireless sensor networks. It first provides background on wormhole attacks, where an attacker tunnels network traffic to another location to compromise routing. It then reviews related work detecting wormholes using cryptography, location verification, or intrusion detection. The paper proposes a system with guard nodes that collaboratively monitor links to detect compromised nodes. It describes modules for network topology establishment, attack establishment through different wormhole modes, and an elimination mechanism where guard nodes isolate attackers once malicious behaviors exceed thresholds. Simulations test the ability of this scheme to improve security against wormhole attacks in resource-constrained wireless sensor networks.
Data Allocation Strategies for Leakage DetectionIOSR Journals
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
A Modular Approach To Intrusion Detection in Homogenous Wireless NetworkIOSR Journals
This document discusses a modular approach to intrusion detection in homogeneous wireless networks. It begins by introducing wireless networks and the need for intrusion detection systems (IDS) due to security vulnerabilities. It then discusses different types of IDS, including signature-based detection that identifies known attacks, and anomaly-based detection that identifies deviations from normal behavior but can result in high false positives. The document proposes a modular approach combining advantages of signature-based and anomaly-based detection for high detection rates and low false positives. Requirements for IDS in wireless networks are also outlined.
Design and Implementation of Single Leg Reduce Switch Count Dual Output Inver...IOSR Journals
This document describes a proposed three-switch single-leg inverter topology that can independently supply two AC loads using reduced semiconductor switches compared to conventional six-switch topologies. The three-switch inverter uses three semiconductor switches and three parallel capacitors to generate independent outputs of varying frequency and amplitude. Simulation and experimental results show that the three-switch inverter can successfully drive two AC loads independently while reducing components, cost, size and weight compared to traditional designs.
The document summarizes a study of the dielectric properties of nano-crystalline Mn-Zn ferrites. Samples of ZnxMn1-xFe2O4 where x ranges from 0.2 to 0.8 were synthesized using a solid-state route and characterized. The ac conductivity σ, dielectric constant ε', dielectric loss ε'', and loss tangent tan δ were measured from 100 Hz to 20 MHz. ε' and ε'' decreased with increasing frequency. σ was nearly frequency independent below 1 MHz and increased sharply above. The maximum dielectric constant and conductivity occurred for x=0.2, attributed to space charge polarization. The conduction mechanism was explained by electron hopping between Fe2+ and Fe
Evaluation of Web Search Engines Based on Ranking of Results and FeaturesWaqas Tariq
Search engines help the user to surf the web. Due to the vast number of web pages it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, Web search ranking algorithms play an important role in ranking web pages so that the user could retrieve the page which is most relevant to the user's query. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five Web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.
This document discusses techniques for detecting link farms, which are groups of web pages that link to each other to artificially boost their PageRank scores. It provides background on PageRank and how link farms can manipulate it. The proposed method calculates both PageRank and a new "GapRank" score for pages, and identifies pages as part of a link farm if they have identical PageRank and GapRank values. The method is demonstrated on a sample dataset, where pages with duplicate PageRank scores are found and shown to also have identical GapRank, identifying them as a link farm that is then removed from the dataset. This improves the PageRank algorithm's ability to rank pages accurately.
IRJET- Page Ranking Algorithms – A ComparisonIRJET Journal
This document compares and contrasts three popular web page ranking algorithms: PageRank, Weighted PageRank, and HITS.
PageRank is the original algorithm used by Google that ranks pages based on the number and quality of inbound links. Weighted PageRank improves on PageRank by assigning different weights to outbound links based on their importance. HITS ranks pages based on whether they serve as hubs that link to many authoritative pages or as authorities that are linked to by many hubs. Each algorithm has advantages like relevance to queries, but also disadvantages like favoring older pages.
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.
The way in which the displaying of the web pages is done within a search is not a mystery. It involves applied math and good computer science knowledge for the right implementation. This relation involves vectors, matrixes and other mathematical notations. The PageRank vector needs to be calculated, that implies calculations for a stationary distribution, stochastic matrix. The matrices hold the link structure and the guidance of the web surfer. As links are added every day, and the number of websites goes beyond billions, the modification of the web link’s structure in the web affects the PageRank. In order to make this work, search algorithms need improvements. Problems and misbehaviors may come into place, but this topic pays attention to many researches which do improvements day by day. Even though it is a simple formula, PageRank runs a successful business. PageRank may be considered as the right example where applied math and computer knowledge can be fitted together.
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.
This lecture discusses the structure of the web, link analysis, and web search. It covers the basic components of a search engine including crawling, indexing, ranking, and query processing. It describes how web crawlers work by recursively fetching links from seed URLs. It also discusses link-based ranking algorithms like PageRank that rank pages based on the link structure of the web. The lecture further covers challenges like spam and approaches to detect web spam like TrustRank, Anti-TrustRank, Spam Mass, and Link Farm spam. The author proposes techniques to refine seed sets and order algorithms to improve web spam filtering.
This document discusses algorithms for detecting link farm spam pages on the web. Link farms are networks of densely interconnected websites that aim to improve search engine rankings. The authors present a method to first generate a seed set of suspected spam pages based on common incoming and outgoing links. They then expand this seed set using an algorithm called ParentPenalty to identify additional pages within link farms. Experimental results show their approach can identify most link farm spam pages and improve search engine rankings by modifying the web graph used in ranking algorithms to discount links within identified link farms.
This document discusses algorithms for detecting link farm spam pages on the web. Link farms are networks of densely interconnected websites that aim to improve search engine rankings. The authors present a method to first generate a seed set of suspected spam pages based on common incoming and outgoing links. They then expand this seed set using an algorithm called ParentPenalty to identify additional pages within link farms. Experimental results show their approach can identify most link farm spam pages and improve search engine rankings by modifying the web graph used in ranking algorithms to discount links within identified link farms.
This document discusses algorithms for detecting link farm spam pages on the web. Link farms are networks of densely interconnected websites that aim to improve search engine rankings. The authors present a method to first generate a seed set of suspected spam pages based on common incoming and outgoing links. They then expand this seed set using an algorithm called ParentPenalty to identify additional pages within link farms. Experimental results show their approach can identify most link farm spam pages and improve search engine rankings by modifying the web graph used in ranking algorithms like HITS and PageRank.
This document compares different ranking algorithms used by search engines. It summarizes PageRank, HITS, SALSA, Weighted PageRank, Distance Rank, and Topic-Sensitive PageRank algorithms. The document analyzes the objectives, inputs, importance, limitations, and applications of each algorithm. It also provides examples and compares the algorithms based on criteria like year of existence, objective, input parameters, importance, limitations, search engines that use them, and quality of results. The proposed work discussed is improving PageRank to address the problem of dangling pages.
Web mining involves applying data mining techniques to automatically discover and extract information from web documents and services. It has three main types: web content mining, which extracts useful information from web document contents; web structure mining, which analyzes the hyperlink structure of websites; and web usage mining, which involves discovering patterns from user interactions on websites. Popular algorithms for web mining include PageRank for web structure mining and HITS for determining both hub and authority pages.
This document provides an overview of a project to build a page ranking tool. It discusses the objective to provide efficient search results by determining the page rank of web pages. It covers topics like how page rank is calculated using a formula, web crawlers, determining page rank for each page using a damping factor and algorithm, the project modules, and related problems like rank sinks and dangling links.
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.
This document proposes techniques to detect web spam pages by using a small set of manually evaluated "seed" pages. It introduces the concept of a "trust rank" algorithm that assigns scores to pages based on their connectivity to seed pages identified as reputable by human experts. The paper presents an evaluation of these techniques on a large web crawl, finding that a seed set of less than 200 sites can effectively filter out spam from a significant portion of the web.
Incremental Page Rank Computation on Evolving Graphs : NOTESSubhajit Sahu
Highlighted notes while doing research work under Prof. Dip Sankar Banerjee and Prof. Kishore Kothapalli:
Incremental Page Rank Computation on Evolving Graphs.
https://dl.acm.org/doi/10.1145/1062745.1062885
This paper describes a simple method for computing dynamic pagerank, based on the fact that change of out-degree of a node does not affect its pagerank (first order markov property). The part of graph which is updated (edge additions / edge deletions / weight changes) is used to find the affected partition of graph using BFS. The unaffected partition is simply scaled, and pagerank computation is done only for the affected partition.
This presentation is based on ranking of web pages, mainly it consist of PageRank algorithm and HITS algorithm. It gives brief knowledge of how to calculate page rank by looking at the links between the pages. It tells you about different techniques of search engine optimization.
The document discusses the history and development of web search engines. It describes how early search engines in 1994 indexed around 100,000 pages while Google grew to index over 8 billion pages by 2005. It also explains the basic components and ranking algorithms of search engines, including PageRank, which calculates the importance of pages based on both the number and quality of inbound links.
Similar to Enhancement in Weighted PageRank Algorithm Using VOL (20)
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This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
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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:
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- Exploiting IAM PassRole Misconfiguration
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Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
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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
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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
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Zener Diode and its V-I Characteristics and Applications
Enhancement in Weighted PageRank Algorithm Using VOL
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 5 (Sep. - Oct. 2013), PP 135-141
www.iosrjournals.org
www.iosrjournals.org 135 | Page
Enhancement in Weighted PageRank Algorithm Using VOL
Sonal Tuteja1
1
(Software Engineering, Delhi Technological University,India)
Abstract: There are billions of web pages available on the World Wide Web (WWW). So there are lots of
search results corresponding to a user’s query out of which only some are relevant. The relevancy of a web
page is calculated by search engines using page ranking algorithms. Most of the page ranking algorithm use
web structure mining and web content mining to calculate the relevancy of a web page. In this paper, the
standard Weighted PageRank algorithm is being modified by incorporating Visits of Links(VOL).The proposed
method takes into account the importance of both the number of visits of inlinks and outlinks of the pages and
distributes rank scores based on the popularity of the pages. So, the resultant pages are displayed on the basis
of user browsing behavior.
Keywords: inlinks, outlinks, search engine, web mining, World Wide Web (WWW).
I. Introduction
WWW has ample number of hyperlinked documents and these documents contain heterogeneous
information including text, image, audio, video, and metadata. Since WWW’s evolution, it has expanded by
2000% and its size is doubling in every six to ten months [1]. For a given query, there are lots of documents
returned by a search engine out of which only few of them are relevant for a user. So the ranking algorithms are
indispensable to sort the results so that more relevant documents are displayed at the top.
Various ranking algorithms have been developed such as PageRank, Weighted PageRank, Page
Content Ranking, and HITS etc. These algorithms are based on web structure mining or web content mining or
combination of both. But web structure mining only considers link structure of the web and web content mining
is not able to cope up with multimedia such as images, mp3 and videos [2]. The proposed method incorporates
VOL with Weighed PageRank to calculate the value of page rank. In this way, the algorithm provides better
results by merging web usage mining with web structure mining.
The organization of the paper is as follows: In section 2, a brief idea about research background has
been given. In Section 3, the proposed work has been described with algorithm and example. Section 4
describes the advantages and disadvantages of proposed work. In section 5, the results of proposed method have
been compared with Weighted PageRank and Weighed PageRank using visits of links. Conclusion and future
work have been given in section 6.
II. Research Background
Data mining can be defined as the process of extracting useful information from large amount of data.
The application of data mining techniques to extract relevant information from the web is called as web mining
[3] [4]. It plays a vital role in web search engines for ranking of web pages and can be divided into three
categories: web structure mining (WSM), web content mining (WCM) and web usage mining (WUM) [3].
WSM is used to extract information from the structure of the web, WCM to mine the content of web pages and
WUM to extract information from the server logs.
Brin and Page [5] came up with an idea at Stanford University to use link structure of the web to
calculate rank of web pages. PageRank algorithm is used by Google to prioritize the results produced by
keyword based search. The algorithm works on the principle that if a web page has important links towards it
then the links of this page to other pages are also considered important. Thus it relies on the backlinks to
calculate the rank of web pages. The page rank is calculated by the formula given in equation 1.
𝑃𝑅 𝑢 = 𝑐
𝑃𝑅 𝑣
Nv
𝑣ɛB u
(1)
Where u represents a web page, 𝑃𝑅 𝑢 and 𝑃𝑅 𝑣 represents the page rank of web pages u and v
respectively, B(u) is the set of web pages pointing to u, Nv represents the total numbers of outlinks of web page
v and c is a factor used for normalization.
Original PageRank algorithm was modified by taking into consideration that not all users follow direct
links on WWW. The modified formula for calculating page rank is given in equation 2.
2. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 136 | Page
𝑃𝑅 𝑢 = 1 − 𝑑 + 𝑑
𝑃𝑅 𝑣
Nv
𝑣ɛB u
(2)
Where d is a dampening factor which represent the probability of user using direct links and it can be
set between 0 and 1.
Wenpu Xing and Ali Ghorbani [6] proposed an algorithm called Weighted PageRank algorithm by
extending standard PageRank. It works on the principle that if a page is important, more linkages from other
web pages have to it or are linked to by it. Unlike standard PageRank, it does not evenly distribute the page rank
of a page among its outgoing linked pages. The page rank of a web page is divided among its outgoing linked
pages in proportional to the importance or popularity (its number of inlinks and outlinks).
Win
(v, u), the popularity from the number of inlinks, is calculated based on the number of inlinks of
page u and the number of inlinks of all reference pages of page v as given in equation 3.
Win
v, u =
𝐼 𝑢
𝐼𝑝𝑝ɛR v
(3)
Where 𝐼 𝑢 and 𝐼𝑝 are the number of inlinks of page u and p respectively. R(v) represents the set of web
pages pointed by v.
Wout
(v, u), the popularity from the number of outlinks, is calculated based on the number of outlinks
of page u and the number of outlinks of all reference pages of page v as given in equation. 4.
Wout
v, u =
𝑂𝑢
𝑂𝑝𝑝ɛR v
(4)
Where 𝑂𝑢 and 𝑂𝑝 are the number of outlinks of page u and p respectively and R(v) represents the set of
web pages pointed by v. The page rank using Weighted PageRank algorithm is calculated by the formula as
given in equation 5.
𝑃𝑅 𝑢 = 1 − 𝑑 + 𝑑 𝑃𝑅 𝑣 Win
𝑣, 𝑢 Wout
𝑣, 𝑢
𝑣ɛB u
5
Gyanendra Kumar et. al. [7] came up with a new idea to incorporate user’s broswsing behavior in
calculating page rank. Previous algorithms were either based on web structure mining or web content mining but
none of them took web usage mining into consideration. A new page ranking algorithm called Page Ranking
based on Visits of Links (VOL) was proposed for search engines. It modifies the basic page ranking algorithm
by taking into consideration the number of visits of inbound links of web pages. It helps to prioritize the web
pages on the basis of user’s browsing behavior.
In the original PageRank algorithm, the rank of a page p is evenly distributed among its outgoing links
but in this algorithm, rank values are assigned in proportional to the number of visits of links. The more rank
value is assigned to the link which is most visited by user. The Page Ranking based on Visits of Links (VOL)
can be calculated by the formula given in equation 6.
𝑃𝑅 𝑢 = 1 − 𝑑 + 𝑑 𝐿 𝑢
𝑃𝑅(𝑣)
𝑇𝐿(𝑣)
𝑣ɛB u
(6)
Where 𝑃𝑅 𝑢 and 𝑃𝑅 𝑣 represent page rank of web pages u and v respectively, d is dampening
factor, B(u) is the set of web pages pointing to u, 𝐿 𝑢 is number of visits of links pointing from v to u, 𝑇𝐿(𝑣) is
the total number of visits of all links from v.
Neelam Tyagi and Simple Sharma [8] incorporated user browsing behavior in Weighted PageRank
algorithm to develop a new algorithm called Weighted PageRank based on number of visits of links (VOL). The
algorithm assigns more rank to the outgoing links having high VOL .It only considers the popularity from the
number of inlinks and ignores the popularity from the number of outlinks which was incorporated in Weighted
PageRank algorithm.
In the original Weighted PageRank algorithm, the page rank of a web page is divided among its
outgoing linked pages in proportional to the importance or popularity (its number of inlinks and outlinks) but in
this algorithm, number of visits of inbound links of web pages are also taken into consideration. The rank of
web page using this algorithm can be calculated as given in equation 7.
3. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 137 | Page
𝑊𝑃𝑅 𝑉𝑂𝐿 𝑢 = 1 − 𝑑 + 𝑑
𝐿 𝑢 𝑊𝑃𝑅 𝑉𝑂𝐿 (𝑣)Win
𝑣, 𝑢
𝑇𝐿(𝑣)
𝑣ɛB u
(7)
Where 𝑊𝑃𝑅 𝑉𝑂𝐿 𝑢 and 𝑊𝑃𝑅 𝑉𝑂𝐿 𝑣 represent page rank of web page u and v respectively, d is the
dampening factor, B(u) is the set of web pages pointing to u, 𝐿 𝑢 is number of visits of links pointing from v to u,
𝑇𝐿(𝑣) is the total number of visits of all links from v, Win
𝑣, 𝑢 represents the popularity from the number of
inlinks of u. Table gives a brief description of above algorithm using some parameters from [9].
Table 1: Comparison of Ranking Algorithms
Algorithm PageRank Weighted PageRank PageRank with VOL
Weighted
PageRank with
VOL
Web mining technique
used
Web structure mining Web structure mining
Web structure mining,
web usage mining
Web structure
mining, web usage
mining
Input Parameters Backlinks Backlinks, Forward links Backlinks and VOL
Backlinks and
VOL
Importance More More More More
Relevancy Less Less More More
III. Proposed work
The original Weighted PageRank algorithm distributes the rank of a web page among its outgoing
linked pages in proportional to their importance or popularity. Win
(v, u), the popularity from the number of
inlinks and Wout
(v, u), the popularity from the number of outlinks does not include usage trends. It does not
give more popularity to the links most visited by the users. The weighted PageRank using VOL makes use of
web structure mining and web usage mining but it neglects the popularity from the number of outlinks i.e.,
Wout
(v, u). In proposed algorithm,WVOL
in
(v, u), the popularity from the number of visits of inlinks
and WVOL
out
(v, u), the popularity from the number of visits of outlinks are used to calculate the value of page rank.
WVOL
in
(v, u) is the weight of link(v, u) which is calculated based on the number of visits of inlinks of page u and
the number of visits of inlinks of all reference pages of page v as given in equation 8.
WVOL
in
v, u =
𝐼 𝑢 𝑉𝑂𝐿
𝐼𝑝 𝑉𝑂𝐿𝑝ɛR v
(8)
Where 𝐼 𝑢(𝑉𝑂𝐿) and 𝐼𝑝(𝑉𝑂𝐿) represents the incoming visits of links of page u and p respectively and R(v)
represents the set of reference pages of page v. WVOL
out
(v, u) is the weight of link(v, u) which is calculated based
on the number of visits of outlinks of page u and the number of visits of outlinks of all reference pages of page v
as given in equation 9.
WVOL
out
v, u =
𝑂𝑢 𝑉𝑂𝐿
𝑂𝑝 𝑉𝑂𝐿𝑝ɛR v
(9)
Where 𝑂𝑢(𝑉𝑂𝐿) and 𝑂𝑝(𝑉𝑂𝐿) represents the outgoing visits of links of page u and respectively and R(v)
represents the set of reference pages of page v. Now these values are used to calculate page rank using equation
10.
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝑢 = 1 − 𝑑 + 𝑑 𝑊𝑃𝑅 𝑉𝑂𝐿 WVOL
in
v, u WVOL
out
v, u
𝑣ɛB u
(10)
Where d is a dampening factor, B(u) is the set of pages that point to u, 𝑊𝑃𝑅 𝑉𝑂𝐿 (𝑢) and 𝑊𝑃𝑅 𝑉𝑂𝐿 (𝑣)
are the rank scores of page u and v respectively, WVOL
in
(v, u) represents the popularity from the number of visits
of inlinks and WVOL
out
(v, u) represents the popularity from the number of visits of outlinks.
3.1. Algorithm to calculate 𝐄𝐖𝐏𝐑 𝐕𝐎𝐋
1. Finding a website: The website with rich hyperlinks is to be selected because the algorithm depends on the
hyper structure of website.
2. Generating a web graph: For selected website, web graph a generated in which nodes represent web pages
and edges represent hyperlinks between web pages.
4. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 138 | Page
3. Calculating number of visits of hyperlinks: Client side script is used to monitor the hits of hyperlinks and
information is sent to the web server and this information is accessed by crawlers.
4. Calculate page rank of each web page: The values of WVOL
in
(v, u), the popularity from the number of visits of
inlinks and WVOL
out
(v, u), the popularity from the number of visits of outlinks are calculated for each node using
formulae given in equation 8 and 9 and these values are substituted in equation 10 to calculate values of page
rank.
5. Repetition of step 4: The step 4 is used recursively until a stable value of page rank is obtained. The Fig. 1
shown below explains the steps required to calculate page rank using proposed algorithm.
Fig 1: Algorithm to calculate 𝐄𝐖𝐏𝐑 𝐕𝐎𝐋
3.2. Example to illustrate the working of proposed algorithm
The working of proposed algorithm has been illustrated via taking a hypothetical web graph having
web pages A, B and C and links representing hyperlinks between pages marked with their number of visits
shown in Fig. 2.
Fig. 2: A web graph
The value of pagerank for web pages A, B and C are calculated using equation 10 as:
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 (𝐴) = 1 − 𝑑 + 𝑑𝑊𝑃𝑅 𝑉𝑂𝐿 𝐶 WVOL
in
𝐶, 𝐴 WVOL
out
(𝐶, 𝐴)
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐵 = 1 − 𝑑 + 𝑑𝑊𝑃𝑅 𝑉𝑂𝐿 𝐴 WVOL
in
𝐴, 𝐵 WVOL
out
(𝐴, 𝐵)
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐶 = 1 − 𝑑 + 𝑑(𝑊𝑃𝑅 𝑉𝑂𝐿 𝐴 WVOL
in
𝐴, 𝐶 WVOL
out
𝐴, 𝐶 + 𝑊𝑃𝑅 𝑉𝑂𝐿 B WVOL
in
𝐵, 𝐶 WVOL
out
(𝐵, 𝐶)
Each intermediate values WVOL
in
𝑣, 𝑢 and WVOL
out
(𝑣, 𝑢) are calculated using equation 8 and 9.
WVOL
in
𝐶, 𝐴 =
𝐼𝐴(𝑉𝑂𝐿)
𝐼𝐴(𝑉𝑂𝐿)
=
2
2
= 1
WVOL
out
𝐶, 𝐴 =
𝑂𝐴(𝑉𝑂𝐿)
𝑂𝐴(𝑉𝑂𝐿)
=
3
3
= 1
WVOL
in
𝐴, 𝐵 =
𝐼 𝐵(𝑉𝑂𝐿)
𝐼 𝐵(𝑉𝑂𝐿) + 𝐼 𝐶(𝑉𝑂𝐿)
=
1
1 + 2
=
1
3
WVOL
out
𝐴, 𝐵 =
𝑂 𝐵(𝑉𝑂𝐿)
𝑂 𝐵(𝑉𝑂𝐿) + 𝑂𝐶(𝑉𝑂𝐿)
=
2
2 + 2
=
2
4
WVOL
in
𝐴, 𝐶 =
𝐼 𝐶(𝑉𝑂𝐿)
𝐼 𝐶(𝑉𝑂𝐿) + 𝐼 𝐵(𝑉𝑂𝐿)
=
4
4 + 1
=
4
5
WVOL
out
𝐴, 𝐶 =
𝑂𝐶(𝑉𝑂𝐿)
𝑂 𝐶(𝑉𝑂𝐿) + 𝑂 𝐵(𝑉𝑂𝐿)
=
2
2 + 2
=
2
4
Findinga
website
Genarating a
web graph
Calculating
number of
visits of
hyperlinks
calculalete
page rank of
each web
page
Repitition of
previous step
until
stabilized
A
B C
1
2
2 2
5. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 139 | Page
WVOL
in
𝐵, 𝐶 =
𝐼 𝐶(𝑉𝑂𝐿)
𝐼 𝐶(𝑉𝑂𝐿)
=
4
4
= 1
WVOL
in
𝐵, 𝐶 =
𝑂𝐶(𝑉𝑂𝐿)
𝑂𝐶(𝑉𝑂𝐿)
=
2
2
= 1
The calculated values are put in above equations to calculate the values of page ranks. For d = 0.35,
page rank values for A, B and C can be calculated as:
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐴 = 0.65 + 0.35 1 ∗
2
2
∗
3
3
= 1
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐵 = 0.65 + 0.35 1 ∗
1
3
∗
2
4
= .70833
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐶 = 0.50 + 0.50 1 ∗
4
5
∗
2
4
+ .70833 ∗
4
4
∗
2
2
= 1.03792
These values are calculated iteratively until the values get stabilized and the final values of page ranks
are: A=1.01406, B=0.70915 and C=1.04017. For d = 0.50, page rank values for A, B and C can be calculated as:
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐴 = 0.50 + 0.50 1 ∗
2
2
∗
3
3
= 1
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐵 = 0.50 + 0.50 1 ∗
1
3
∗
2
4
= 0.58333
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐶 = 0.50 + 0.50 1 ∗
4
5
∗
2
4
+ .58333 ∗
4
4
∗
2
2
= 0.99167
These values are calculated iteratively until the values get stabilized and the final values of page ranks
are: A=0.99527, B=0.58294 and C=0.99052. For d = 0.85, page rank values for A, B and C can be calculated as:
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐴 = 0.15 + 0.85 1 ∗
2
2
∗
3
3
= 1
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐵 = 0.15 + 0.85 1 ∗
1
3
∗
2
4
= 0.29167
𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 𝐶 = 0.50 + 0.50 1 ∗
4
5
∗
2
4
+ .29167 ∗
4
4
∗
2
2
= 0.73792
These values are calculated iteratively until the values get stabilized and the final values of page ranks
are: A=0.63531, B=0.24 and C=0.57001. The value of page rank at various values of d has been given in Table.
Table 2: Value of page ranks at different d values
d A B C
0.35 1.01406 0.70915 1.04017
0.50 0.99527 0.58294 0.99052
0.85 0.63531 0.24001 0.57001
IV. Advantages and Disadvantages
The proposed method includes web usage mining to calculate the page ranks of web pages and has
following advantages.
The page rank using original WPR remains unaffected whether the page has been accessed by the users or
not. i.e.; the relevancy of a web page is ignored. But the page rank using proposed method 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿
assigns high rank to web pages having more visits of links.
The page rank using original WPR depends only on the link structure of the web and remains same whether
the web page has been accessed by the user or not. Although the algorithm 𝑊𝑃𝑅 𝑉𝑂𝐿 makes use of web
structure mining and web usage mining to calculate the value of page rank but it ignores the popularity from
the number of outlinks Wout
(v, u). On the other side, our proposed method 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 makes use
6. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 140 | Page
of WVOL
in
𝑣, 𝑢 , the popularity from the number of visits of inlinks and WVOL
out
𝐴, 𝐶 , the popularity from the
number of visits of outlinks to calculate page rank.
The proposed method uses number of visits of links to calculate the rank of web pages. So the resultant
pages are popular and more relevant to the users need.
The proposed method includes number of visits of links of web pages to calculate page ranks. Suppose
there is a junk page whose initial page rank is high then the users will access it and it will lead to increase in
VOL which will further improve page rank. So the pages which are actually relevant will have less page rank
than junk pages. So some other usage behavior factors must be introduced in addition to VOL. These factors
are:
Time spent on web page corresponding to a link: The algorithm must assign more weight to the link if more
time is spent by the users on the web page corresponding to that link. Most of the times, the time spent on
the junk pages is very less as compared to relevant pages. So this factor will help in lowering the rank of
junk pages.
Most recent use of link: The link which is used most recently by users should have more priority than the
link which has been not used so far. So most recent use of link can also be used to calculate the page rank.
Information about the user: A web page is not equally relevant for all the users. Due to different
requirements of different users, a web page may be more important for one but not for other. Some kind of
users’ information like age, gender, educational background can be used to categorize web pages according
to different users’ need.
V. Result Analysis
This section compares the page rank of web pages using standard Weighted PageRank (WPR),
Weighted PageRank using VOL (𝑊𝑃𝑅 𝑉𝑂𝐿 ) and the proposed algorithm. We have calculated rank value of each
page based on WPR, 𝑊𝑃𝑅 𝑉𝑂𝐿 and proposed algorithm i.e. 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 for a web graph shown in Fig. 2.
As the value of dampening factor increases, the page rank decreases. The comparison of results is
shown in Table which shows the values of page rank using WPR, 𝑊𝑃𝑅 𝑉𝑂𝐿 and 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 at different d values
of 0.35, 0.50 and 0.85.
Table 3: Comparison of page ranks using different algorithms
The values of page rank using WPR, 𝑊𝑃𝑅 𝑉𝑂𝐿 and 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 have been compared using a bar chart.
The values retrieved by 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 are better than original WPR and 𝑊𝑃𝑅 𝑉𝑂𝐿. The WPR uses only web
structure mining to calculate the value of page rank, 𝑊𝑃𝑅 𝑉𝑂𝐿 uses both web structure mining and web usage
mining to calculate value of page rank but it uses popularity only from the number of inlinks not from the
number of outlinks. The proposed algorithm 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 method uses number of visits of inlinks and outlinks to
calculate values of page rank and gives more rank to important pages. Fig. 3 compares the page ranks of A, B
and C using WPR, 𝑊𝑃𝑅 𝑉𝑂𝐿 and 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 for d=0.35.
Fig. 3: Comparison of page ranks at d=0.35
0
0.2
0.4
0.6
0.8
1
1.2
A B C
WPR
WPR(VOL)
EWPR(VOL)
d 0.35 0.50 0.85
WPR
A 1.00535 0.97677 0.58335
B 0.70865 0.58140 0.23335
C 1.01532 0.95351 0.51505
WPR(VOL)
A 1.01736 1 0.64037
B 0.68956 0.55556 0.21495
C 1.04960 1 0.57463
EWPR(VOL)
A 1.01406 0.99527 0.63531
B 0.70915 0.58140 0.23335
C 1.04017 0.99052 0.57001
7. Enhancement in Weighted PageRank Algorithm Using VOL
www.iosrjournals.org 141 | Page
Fig. 4 and Fig. 5 compares the page ranks of A, B and C using WPR, 𝑊𝑃𝑅 𝑉𝑂𝐿 and 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 for d=0.50.
Fig. 4: Comparison of page ranks at d=0.50
Fig. 5: Comparison of page ranks at d=0.85
VI. Conclusions and Future Work
Due to enormous amount of information present on the web, the users have to spend lot of time to get
pages relevant to them. So the proposed algorithm 𝐸𝑊𝑃𝑅 𝑉𝑂𝐿 makes use of number of visits of links (VOL) to
calculate the values of page rank so that more relevant results are retrieved first. In this way, it may help users to
get the relevant information quickly. Some of the future works for the proposed algorithm are:
The values of page rank have been calculated on a small web graph only. A web graph with large number
of websites and hyperlinks should be used to check the accuracy and importance of method.
We need some other measures like most recent use of link, information about the user and time spent on
web page corresponding to a link. So the future work includes deriving a formula for page rank using these
parameters also.
References
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Brunswick,Fredericton, NB, E3B 5A3, Canada.
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Computer Engineering, YMCA University of Science & Technology, Faridabad, India.
[4] Naresh Barsagade, "Web Usage Mining And Pattern Discovery: A Survey Paper", CSE 8331, Dec.8, 2003.
[5] Dell Zhang, Yisheng Dong, “A novel Web usage mining approach for search engines”, Computer Networks 39 (2002) 303–310
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IACC 2009 IEEE International.
[7] R.Cooley, B.Mobasher and J.Srivastava, "Web Mining: Information and Pattern Discovery on the World Wide Web". In Proceedings
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[8] Companion slides for the text by Dr. M. H. Dunham, "Data Mining: Introductory and Advanced Topics", Prentice Hall, 2002.
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Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012
0
0.2
0.4
0.6
0.8
1
1.2
A B C
WPR
WPR(VOL)
EWPR(VOL)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
A B C
WPR
WPR(VOL)
EWPR(VOL)