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
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
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
COMPARISON ANALYSIS OF WEB USAGE MINING USING PATTERN RECOGNITION TECHNIQUESIJDKP
This document summarizes a research paper that analyzes web usage mining using pattern recognition techniques. It discusses how web logs from the NASA website were preprocessed and then analyzed using a web log exploration tool. Key patterns discovered include determining most visitors were from the US/Canada based on IP addresses, most common file type accessed was images (especially GIF files), and GIF files were most popular on Thursdays at noon. The paper concludes the techniques help understand user behavior and improve website performance and organization strategies.
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
Integrated Web Recommendation Model with Improved Weighted Association Rule M...ijdkp
World Wide Web plays a significant role in human life. It requires a technological improvement to satisfy
the user needs. Web log data is essential for improving the performance of the web. It contains large,
heterogeneous and diverse data. Analyzing g the web log data is a tedious process for Web developers,
Web designers, technologists and end users. In this work, a new weighted association mining algorithm is
developed to identify the best association rules that are useful for web site restructuring and
recommendation that reduces false visit and improve users’ navigation behavior. The algorithm finds the
frequent item set from a large uncertain database. Frequent scanning of database in each time is the
problem with the existing algorithms which leads to complex output set and time consuming process. The
proposed algorithm scans the database only once at the beginning of the process and the generated
frequent item sets, which are stored into the database. The evaluation parameters such as support,
confidence, lift and number of rules are considered to analyze the performance of proposed algorithm and
traditional association mining algorithm. The new algorithm produced best result that helps the developer
to restructure their website in a way to meet the requirements of the end user within short time span.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
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
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
COMPARISON ANALYSIS OF WEB USAGE MINING USING PATTERN RECOGNITION TECHNIQUESIJDKP
This document summarizes a research paper that analyzes web usage mining using pattern recognition techniques. It discusses how web logs from the NASA website were preprocessed and then analyzed using a web log exploration tool. Key patterns discovered include determining most visitors were from the US/Canada based on IP addresses, most common file type accessed was images (especially GIF files), and GIF files were most popular on Thursdays at noon. The paper concludes the techniques help understand user behavior and improve website performance and organization strategies.
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
Integrated Web Recommendation Model with Improved Weighted Association Rule M...ijdkp
World Wide Web plays a significant role in human life. It requires a technological improvement to satisfy
the user needs. Web log data is essential for improving the performance of the web. It contains large,
heterogeneous and diverse data. Analyzing g the web log data is a tedious process for Web developers,
Web designers, technologists and end users. In this work, a new weighted association mining algorithm is
developed to identify the best association rules that are useful for web site restructuring and
recommendation that reduces false visit and improve users’ navigation behavior. The algorithm finds the
frequent item set from a large uncertain database. Frequent scanning of database in each time is the
problem with the existing algorithms which leads to complex output set and time consuming process. The
proposed algorithm scans the database only once at the beginning of the process and the generated
frequent item sets, which are stored into the database. The evaluation parameters such as support,
confidence, lift and number of rules are considered to analyze the performance of proposed algorithm and
traditional association mining algorithm. The new algorithm produced best result that helps the developer
to restructure their website in a way to meet the requirements of the end user within short time span.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
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.
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 describes a system called UProRevs that aims to personalize web search results based on a user's profile and interests.
The system works as a filter that takes the results from a normal search engine like Google and re-ranks them based on their relevance to the user's profile. It generates user profiles based on information provided during registration, and updates the profiles over time based on the user's feedback on search results.
The system calculates relevance scores for search results by comparing the keywords in each web page to those in the user's profile. Results are displayed along with their relevance scores. As the user provides feedback, their profile is updated, allowing the system to continuously improve the personalization of search
Comparable Analysis of Web Mining Categoriestheijes
Web Data Mining is the current field of analysis which is a combination of two research area known as Data Mining and World Wide Web. Web Data Mining research associates with various research diversities like Database, Artificial Intelligence and Information redeem. The mining techniques are categorized into various categories namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this work, analysis of mining techniques are done. From the analysis it has been concluded that Web Content Mining has unstructured or semi- structure view of data whereas Web Structure Mining have linked structure and Web Usage Mining mainly includes interaction.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
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.
COLLABORATIVE BIBLIOGRAPHIC SYSTEM FOR REVIEW/SURVEY ARTICLESijcsit
This paper proposes a Bibliographic system intends to exchange bibliographic information of survey/review articles by relying on Web service technology. It allows researchers and university students
to interact with system via single service using platform-independent standard named Web service to add,
search and retrieve bibliographic information of review articles in various science and technology fields
and build-up a dedicated database for these articles in each science and technology field. Additionally,
different implementation scenarios of the proposed system are presented and described, andrich features
that offered by such system are studied and described. However, this paper explains the proposed system
using computing area due to the existence of detailed taxonomy of this area, which allows defining the
system, their functionalities and features provided.However, the proposed system is not only confined to
computing area, it can support any other science and technology area without any need to modify this
system.
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.
A Novel Method for Data Cleaning and User- Session Identification for Web MiningIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Web mining involves analyzing textual and link structure data from the world wide web to discover useful information. It deals with petabytes of data generated daily and needs to adapt to evolving usage patterns in real-time. Topics related to web mining include web graph analysis, power laws, structured data extraction, web advertising, user analysis, social networks, and blog analysis. The future will involve very large-scale data mining of datasets too big to fit in memory or even on a single disk.
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.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
SEMINAR_REPORT -Model-View-Controller design architecture to develop web base...SURAJ NAYAK
THIS SEMINAR PAPER WAS PREPARED BY ME FOR MY SEMINAR .I AM UPLOADING IT SO THAT MORE PEOPLE CAN BE BENEFITED FROM THIS.HOPE ITS SOME USE TO IT.LIKE THE DOCUMENT...#ENGINEERS
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.
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.
a novel technique to pre-process web log data using sql server management studioINFOGAIN PUBLICATION
This document summarizes a research paper that proposes a novel technique for pre-processing web log data using SQL Server Management Studio. The paper first discusses how web log data contains irrelevant information that needs to be cleaned through pre-processing before analysis. It then describes the contents of a typical web log file and provides a sample of raw web log data. The paper presents an algorithm for data cleaning and implements it using SQL queries to clean the web log data by removing records with certain file extensions and incomplete URLs. It shows that the data was reduced from over 200,000 records to around 25,000 after pre-processing. The paper concludes that pre-processing is an important step for filtering and organizing data before applying data mining techniques.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
DISCOVERABILITY A NEW LEARNABILITY PRINCIPLE FOR CHILDREN’S APPLICATION SOFTWAREijcsit
For more than two decades children’s use of multimedia was restricted to watching television and listening
to music. Although some parents complained about children being addicted to listening to music the idea
that children could be addicted to television was a real concern to most parents. Nowadays parents not
only need to be concerned about how much television their kids are watching, but also many other forms of
media that are emerging with the fast development in information and technology such as the internet,
video games, tablets and smart phones. From this the researcher came to realize that children are
increasingly becoming the consumers of application software facilitated by these information systems.
Children spend at least three hours according to research on these media which includes the use of
computers, tablets, smartphones and music. The researcher was concerned that system vendors use the
same learnability principles to make applications for all age groups based on learnability principles that
were designed with adult users in mind. Many interface design principles used for adult products cannot be
applied to products meant for children and further yet children at different ages learn differently. The
research looked at the existing learnability principles by trying to evaluate them and come up with new
principle(s) that can be used to further improve the current principles so that they can be used effectively
by information system designers to improve on the learna
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
ADAPTIVE WATERMARKING TECHNIQUE FOR SPEECH SIGNAL AUTHENTICATION ijcsit
Biometrics data recently has become a major role in determining the identity of the person. With such
importance for the use of biometrics data, there are many attacks that threaten the security and integrity of
biometrics data itself. Therefore, it becomes necessary to protect the originality of biometrics data against
manipulation and fraud. This paper presents an authentication technique to achieve the authenticity of
speech signals based on adaptive watermarking technique. The basic idea is depends on extracting the
speech features from the speech signal initially and then using these features as a watermark. The
watermark information embeds into the same speech signal. The short time energy technique is used to
identifying the suitable positions for embedding the watermark in order to avoid the regions that used in
the speech recognition system. After exclusion the important areas that used in speech recognition the
Genetic Algorithm (GA) is used to generate random locations to hide the watermark information in an
intelligent manner. The experimental results have achieved high efficiency in establishing the authenticity
of speech signal and the process of embedding
RESEARCH REVIEW FOR POSSIBLE RELATION BETWEEN MOBILE PHONE REDIATION AND BRAI...ijitcs
The aim of this paper is to introduce a research review for the effect of Mobile phone radiation on human
health and the possible relation between Mobile phone radiation and brain tumor. Mobile phones become
increasingly prevalent throughout our society. In the year 2016, it was estimated that there were 4 billion
cellular phone users worldwide; the number is growing by one million every month in the US. The goal of
this paper is to give a brief overview and also discuss the biological effects of the exposure to mobile
phones radiation. Many effects have been reported with the use of mobile phones on human organisms due
to the exposure to electromagnetic radiation. Concerns about the links between using the mobile phones
and biological effects, in particular, the brain tumors have been under research. As the other radio signals
transmition devices, cellular phone emits radiofrequency energy, which can heat the brain tissues and
cause damage to the brain cells. But even mobile phones operates at power level below the level at which
such heating effects occur, long term exposure to low level RF from mobile phones could cause other types
of health effects, such as brain cancer, due to energy absorption in the brain tissues. Some human
biological experiments, such as Aly et al. 2014, Aly, et al. 2008 indicates, the average time for the human
cells to respond to the effect of RF radiation was approximately 2.5 min, Hardell et al.2002, and Repacholi
et al.1997 indicated increased risk with exposure to mobile phones radiation. The British Association
festival of science was told recently that using a mobile telephone mor
A REPLICATED ASSESSMENT OF THE CRITICAL SUCCESS FACTORS FOR THE ADOPTION OF M...ijcsit
Previous research indicates that there is a failure in the adoption of e-government services to citizens as
planned in the context of developing countries. Obstacles behind this failure are varied, including sociocultural,
economic and technical obstacles. But with recent advances in mobile technologies as well as the
pervasive penetration of mobile phones, governments in developing countries including Jordan have been
able to overcome most of these obstacles through the so-called mobile government (or m-government). This
has provided an alternative channel for governments to improve the interaction with their citizens, as well
as the quality of services provided to them. Accordingly, the exploration of the factors that affect the
adoption of m-government services would reduce the gap between government strategies and policies
relating to the development of m-government services on the one hand, and the perceptions of citizens on
the other hand, allowing for a better understanding of citizens' needs and priorities that must be taken into
account by the governments in order to ensure the success of such services on a large scale. This research
is based on a re-evaluation of the empirical results of a comprehensive study conducted by Susanto and
Goodwin (2010), which concludes that there are fifteen factors that are likely to affect citizens in 25
countries around the world to adopt SMS-based e-g
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.
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 describes a system called UProRevs that aims to personalize web search results based on a user's profile and interests.
The system works as a filter that takes the results from a normal search engine like Google and re-ranks them based on their relevance to the user's profile. It generates user profiles based on information provided during registration, and updates the profiles over time based on the user's feedback on search results.
The system calculates relevance scores for search results by comparing the keywords in each web page to those in the user's profile. Results are displayed along with their relevance scores. As the user provides feedback, their profile is updated, allowing the system to continuously improve the personalization of search
Comparable Analysis of Web Mining Categoriestheijes
Web Data Mining is the current field of analysis which is a combination of two research area known as Data Mining and World Wide Web. Web Data Mining research associates with various research diversities like Database, Artificial Intelligence and Information redeem. The mining techniques are categorized into various categories namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this work, analysis of mining techniques are done. From the analysis it has been concluded that Web Content Mining has unstructured or semi- structure view of data whereas Web Structure Mining have linked structure and Web Usage Mining mainly includes interaction.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
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.
COLLABORATIVE BIBLIOGRAPHIC SYSTEM FOR REVIEW/SURVEY ARTICLESijcsit
This paper proposes a Bibliographic system intends to exchange bibliographic information of survey/review articles by relying on Web service technology. It allows researchers and university students
to interact with system via single service using platform-independent standard named Web service to add,
search and retrieve bibliographic information of review articles in various science and technology fields
and build-up a dedicated database for these articles in each science and technology field. Additionally,
different implementation scenarios of the proposed system are presented and described, andrich features
that offered by such system are studied and described. However, this paper explains the proposed system
using computing area due to the existence of detailed taxonomy of this area, which allows defining the
system, their functionalities and features provided.However, the proposed system is not only confined to
computing area, it can support any other science and technology area without any need to modify this
system.
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.
A Novel Method for Data Cleaning and User- Session Identification for Web MiningIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Web mining involves analyzing textual and link structure data from the world wide web to discover useful information. It deals with petabytes of data generated daily and needs to adapt to evolving usage patterns in real-time. Topics related to web mining include web graph analysis, power laws, structured data extraction, web advertising, user analysis, social networks, and blog analysis. The future will involve very large-scale data mining of datasets too big to fit in memory or even on a single disk.
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.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
SEMINAR_REPORT -Model-View-Controller design architecture to develop web base...SURAJ NAYAK
THIS SEMINAR PAPER WAS PREPARED BY ME FOR MY SEMINAR .I AM UPLOADING IT SO THAT MORE PEOPLE CAN BE BENEFITED FROM THIS.HOPE ITS SOME USE TO IT.LIKE THE DOCUMENT...#ENGINEERS
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.
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.
a novel technique to pre-process web log data using sql server management studioINFOGAIN PUBLICATION
This document summarizes a research paper that proposes a novel technique for pre-processing web log data using SQL Server Management Studio. The paper first discusses how web log data contains irrelevant information that needs to be cleaned through pre-processing before analysis. It then describes the contents of a typical web log file and provides a sample of raw web log data. The paper presents an algorithm for data cleaning and implements it using SQL queries to clean the web log data by removing records with certain file extensions and incomplete URLs. It shows that the data was reduced from over 200,000 records to around 25,000 after pre-processing. The paper concludes that pre-processing is an important step for filtering and organizing data before applying data mining techniques.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
DISCOVERABILITY A NEW LEARNABILITY PRINCIPLE FOR CHILDREN’S APPLICATION SOFTWAREijcsit
For more than two decades children’s use of multimedia was restricted to watching television and listening
to music. Although some parents complained about children being addicted to listening to music the idea
that children could be addicted to television was a real concern to most parents. Nowadays parents not
only need to be concerned about how much television their kids are watching, but also many other forms of
media that are emerging with the fast development in information and technology such as the internet,
video games, tablets and smart phones. From this the researcher came to realize that children are
increasingly becoming the consumers of application software facilitated by these information systems.
Children spend at least three hours according to research on these media which includes the use of
computers, tablets, smartphones and music. The researcher was concerned that system vendors use the
same learnability principles to make applications for all age groups based on learnability principles that
were designed with adult users in mind. Many interface design principles used for adult products cannot be
applied to products meant for children and further yet children at different ages learn differently. The
research looked at the existing learnability principles by trying to evaluate them and come up with new
principle(s) that can be used to further improve the current principles so that they can be used effectively
by information system designers to improve on the learna
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
ADAPTIVE WATERMARKING TECHNIQUE FOR SPEECH SIGNAL AUTHENTICATION ijcsit
Biometrics data recently has become a major role in determining the identity of the person. With such
importance for the use of biometrics data, there are many attacks that threaten the security and integrity of
biometrics data itself. Therefore, it becomes necessary to protect the originality of biometrics data against
manipulation and fraud. This paper presents an authentication technique to achieve the authenticity of
speech signals based on adaptive watermarking technique. The basic idea is depends on extracting the
speech features from the speech signal initially and then using these features as a watermark. The
watermark information embeds into the same speech signal. The short time energy technique is used to
identifying the suitable positions for embedding the watermark in order to avoid the regions that used in
the speech recognition system. After exclusion the important areas that used in speech recognition the
Genetic Algorithm (GA) is used to generate random locations to hide the watermark information in an
intelligent manner. The experimental results have achieved high efficiency in establishing the authenticity
of speech signal and the process of embedding
RESEARCH REVIEW FOR POSSIBLE RELATION BETWEEN MOBILE PHONE REDIATION AND BRAI...ijitcs
The aim of this paper is to introduce a research review for the effect of Mobile phone radiation on human
health and the possible relation between Mobile phone radiation and brain tumor. Mobile phones become
increasingly prevalent throughout our society. In the year 2016, it was estimated that there were 4 billion
cellular phone users worldwide; the number is growing by one million every month in the US. The goal of
this paper is to give a brief overview and also discuss the biological effects of the exposure to mobile
phones radiation. Many effects have been reported with the use of mobile phones on human organisms due
to the exposure to electromagnetic radiation. Concerns about the links between using the mobile phones
and biological effects, in particular, the brain tumors have been under research. As the other radio signals
transmition devices, cellular phone emits radiofrequency energy, which can heat the brain tissues and
cause damage to the brain cells. But even mobile phones operates at power level below the level at which
such heating effects occur, long term exposure to low level RF from mobile phones could cause other types
of health effects, such as brain cancer, due to energy absorption in the brain tissues. Some human
biological experiments, such as Aly et al. 2014, Aly, et al. 2008 indicates, the average time for the human
cells to respond to the effect of RF radiation was approximately 2.5 min, Hardell et al.2002, and Repacholi
et al.1997 indicated increased risk with exposure to mobile phones radiation. The British Association
festival of science was told recently that using a mobile telephone mor
A REPLICATED ASSESSMENT OF THE CRITICAL SUCCESS FACTORS FOR THE ADOPTION OF M...ijcsit
Previous research indicates that there is a failure in the adoption of e-government services to citizens as
planned in the context of developing countries. Obstacles behind this failure are varied, including sociocultural,
economic and technical obstacles. But with recent advances in mobile technologies as well as the
pervasive penetration of mobile phones, governments in developing countries including Jordan have been
able to overcome most of these obstacles through the so-called mobile government (or m-government). This
has provided an alternative channel for governments to improve the interaction with their citizens, as well
as the quality of services provided to them. Accordingly, the exploration of the factors that affect the
adoption of m-government services would reduce the gap between government strategies and policies
relating to the development of m-government services on the one hand, and the perceptions of citizens on
the other hand, allowing for a better understanding of citizens' needs and priorities that must be taken into
account by the governments in order to ensure the success of such services on a large scale. This research
is based on a re-evaluation of the empirical results of a comprehensive study conducted by Susanto and
Goodwin (2010), which concludes that there are fifteen factors that are likely to affect citizens in 25
countries around the world to adopt SMS-based e-g
CELL TRACKING QUALITY COMPARISON BETWEEN ACTIVE SHAPE MODEL (ASM) AND ACTIVE ...ijitcs
The aim of this paper is to introduce a comparison between cell tracking using active shape model (ASM)
and active appearance model (AAM) algorithms, to compare the cells tracking quality between the two
methods to track the mobility of the living cells. Where sensitive and accurate cell tracking system is
essential to cell motility studies. The active shape model (ASM) and active appearance model (AAM)
algorithms has proved to be a successful methods for matching statistical models. The experimental results
indicate the ability of (AAM) meth
A SURVEY ON VARIOUS APPROACHES TO FINGERPRINT MATCHING FOR PERSONAL VERIFICAT...IJCSES Journal
Automatic Fingerprint authentication for personal identification and verification has received considerable
attention over the past decades among various biometric techniques because of the distinctiveness and
persistence properties of fingerprints. Now fingerprints are set to explode in popularity as they are being
used to secure smart phones and to authorize payments in online stores. The main objective of this paper is
to review the extensive research work that has been done over the past decade and discuss the various
approaches proposed for fingerprint matching. The proposed methods were based on 2D correlation in the
spatial and frequency domains, Artificial Neural Networks, Hough transform, Fourier transform, graphs,
local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also
provides the performance comparison of several existing methods proposed by researchers in fing
VIEW OF MEMORY ALLOCATION AND MANAGEMENT IN COMPUTER SYSTEMScseij
This document summarizes memory allocation and management techniques in computer systems. It compares uniprogramming and multiprogramming systems, discussing how multiprogramming improves processor utilization by allowing multiple processes to occupy memory simultaneously. It describes different types of queues used in memory and processor scheduling. It also discusses fixed and variable partitioning for allocating memory to processes, noting variable partitioning is more advantageous as it allows reallocating memory. Finally, it provides an example of virtual to physical address mapping in MIPS R2/3000 architecture.
This document summarizes a research paper that presents a method for vehicle recognition using background subtraction and support vector machines (SVM). It first uses the ViBe background subtraction algorithm to extract foreground objects from video surveillance footage of a parking lot. Histogram of oriented gradients (HOG) features are then extracted from regions of interest and fed into an SVM classifier to determine if the objects are vehicles. The system was able to accurately recognize vehicles in testing with a recognition rate meeting industrial standards.
THE VICES OF SOCIAL MEDIA ON STUDENTS SUCCESS AT THE ADAMAWA STATE POLYTECHNI...ijcsit
Social media and social networking web sites have continued to gain supremacy in determining the
student’s success in education. It captures the attention of students and their tutors over the years. A social
media network is only an electronic links amongst its users which turned out to be a habit for students,
youngsters,
and even the grown person. The influence of social media on students is alarming and doing more harm
than good. The aim of the paper is to analyze the vices of social media on the academic success of students
of Adamawa State Polytechnic, Yola. The survey method of research was adopted to achieve the objectives
of the study. Some research questions were presented to the respondents where the results revealed that so
many (96 percent of them) usually spent much time on the social networking sites than to their academics.
As such, the academic success of the students suffers setbacks which lead to poor performance in the
student’s academics. It is therefore recommended that the networking sites should be built in such a way to
support student’s educational activities in a positive way, as this will help in attracting the students to ge
Social Media Posts On Platforms Such As Twitter Or Instagram Use Hashtags,Which Are Author-Created
Labels Representing Topics Or Themes, Toassist In Categorization Of Posts And Searches For Posts Of
Interest. The Structural Analysis Of Hashtags Is Necessary As Precursor To Understandingtheir Meanings.
This Paper Describes Our Work On Segmenting Nondelimited Strings Of Hashtag-Type English Text. We
Adapt And Extend Methods Used Mostly In Non-Eng
WRITER RECOGNITION FOR SOUTH INDIAN LANGUAGES USING STATISTICAL FEATURE EXTRA...ijnlc
The comprehension of whole manually written records is a testing issue which incorporates various
difficult undertakings. Given a written by hand archive, its format needs to be dissected to detach different
content sorts in a first step. These different content sorts can then be coordinated to specific frameworks,
including writer style, image, or table recognizers. Research in programmed author recognizable proof has
principally centred around the measurable methodology. This has prompted the particular and extraction
of factual elements, for example, run-length appropriations, incline dissemination, entropy, and edge-pivot
conveyance. The edge-pivot conveyance highlight out flanks all other measurable elements. Edge-pivot
circulation is an element that portrays the adjustments in bearing of a written work stroke in written by
hand content. The edge- pivot circulation is extricated by method for a window that is slid over an edgerecognized
on offline scanned images. At whatever point the focal pixel of the window is on, the two edge
pieces (i.e. associated successions of pixels) rising up out of this focal pixel are considered. Their bearings
are measured and put away as sets. A joint likelihood dissemination is gotten from an extensive recognition
In past years of research efforts many have tried to implement an automated system based on time
management. But besides calendaring, communication and collaboration event is negotiated. Based on
experience in developing and deploying an automated event scheduling system, this paper sets the
requirements for an intelligent calendar scheduler that retrieves emails from Gmail, and schedules all of
them by taking into account of the natural language and dissolving certain ambiguities. We outline all these
issues and schedule and proper calendar for the user and also change th
TRAFFIC CONTROL MANAGEMENT AND ROAD SAFETY USING VEHICLE TO VEHICLE DATA TRAN...ijcseit
This document discusses using Li-Fi technology for traffic control management and road safety through vehicle-to-vehicle data transmission. It proposes a system where vehicles' headlights, taillights, and traffic lights are equipped with Li-Fi to transmit and receive data. Data such as maximum speed limits or traffic signal information could be sent to vehicles to manage traffic and reduce accidents. If a vehicle violates traffic rules or gets too close to another, its identification data would be sent to a central server to take legal action or warn the other vehicle to slow down. The system aims to improve road safety through vehicle communication using Li-Fi technology.
IMPACT OF RESOURCE MANAGEMENT AND SCALABILITY ON PERFORMANCE OF CLOUD APPLICA...IJCSEA Journal
Cloud computing facilitates service providers to rent their computing capabilities for deploying
applications depending on user requirements. Applications of cloud have diverse composition,
configuration and deployment requirements. Quantifying the performance of applications in Cloud
computing environments is a challenging task. In this paper, we try to identify various parameters
associated with performance of
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSijcsit
The paper proposes a method to check whether two documents are having textual plagiarism or not. This
technique is based on Extrinsic Plagiarism Detection. The technique that is applied here is quite similar to
the one that is used to grade the short answers. The triplets and associated information are extracted from
both texts and are stored in friendship matrices. Then these two friendship matrices are compared and a
similarity percentage is calculated. This similarity percentage is used to take decisions
RAMEWORKS FOR CLOUD COMPUTING: A CRITICAL REVIEWijcsit
Cloud computing technology has experienced exponential growth over the past few years. It provides many
advantages for both individuals and organizations. However, at the same time, many issues have arisen due
to the vast growth of cloud computing. Organizations often have concerns about the migration and
utilization of cloud computing due to the loss of control over their outsourced resources and cloud
computing is vulnerable to risks. Thus, a cloud provider needs to manage the cloud computing environment
risks in order to identify, assess, and prioritize the risks in order to decrease those risks, improve security,
increase confidence in cloud services, and relieve organizations’ concerns on the issue of using a cloud
environment. Considering that a conventional risk management framework does not fit well with cloud
computing due to the complexity of its environment, research in this area has become widespread. The aim
of this paper is to review the previously proposed risk management frameworks for cloud computing and to
make a comparison between them in order to determine the strengths and weaknesses of each of them. The
review will consider the extent of the inv
SYSTEMATIC LITERATURE REVIEW ON RESOURCE ALLOCATION AND RESOURCE SCHEDULING I...ijait
he objective the work is intend to highlight the key features and afford finest future directions in the
research community of Resource Allocation, Resource Scheduling and Resource management from 2009 to
2016. Exemplifying how research on Resource Allocation, Resource Scheduling and Resource management
has progressively increased in the past decade by inspecting articles, papers from scientific and standard
publications. Survey materialized in three fold process. Firstly, investigate on the amalgamation of
Resource Allocation, Resource Scheduling and then proceeded with Resource management. Secondly, we
performed a structural analysis on different author’s prominent contributions in the form of tabulation by
categories and graphical representation. Thirdly, huddle with conceptual similarity in the field and also
impart a summary on all resource allocations. In cloud computing environments, there are two players:
cloud providers and cloud users. On one hand, providers hold massive computing resources in their large
datacenters and rent resources out to users on a per-usage basis. On the other hand, there are users who
In recent years, research efforts tried to exploit peer-to-peer (P2P) systems in order to provide Live Streaming (LS) and Video-on-Demand (VoD) services. Most of these research efforts focus on the development of distributed P2P block schedulers for content exchange among the participating peers and on the characteristics of the overlay graph (P2P overlay) that interconnects the set of these peers.Currently, researchers try to combine peer-to-peer systems with cloud infrastructures. They developed monitoring and control architectures that use resources from the cloud in order to enhance QoS and achieve an attractive trade-off between stability and low cost operation. However, there is a lack of
research effort on the congestion control of these systems and the existing congestion control architectures are not suitable for P2P live streaming traffic (small sequential non persistent traffic towards multiple network locations). This paper proposes a P2P live streaming traffic aware congestion control protocol that: i) is capable to manage sequential traffic heading to multiple network destinations , ii) efficiently exploits the available bandwidth, iii) accurately measures the idle peer resources, iv) avoids network congestion, and v) is friendly to traditional TCP generated traffic.The proposed P2P congestion control has been implemented, tested and evaluated through a series of real experiments powered across the BonFIRE infrastructure.
Social navigation can be considered as an effective approach for supporting information management issues particularly privacy and security concern that prevail in the management of information systems. Any of these management information system security issues are a matter of critical acknowledgement for knowledge management systems as well. This paper outlines multiple privacy and security risks that can be applied to information systems in general. Including examples from different sectors such as health care, public information, and e-commerce, these management information issues provide an outline of the present situation. It also observes the key concerns of information system executive in these areas, highlighting the identification and explanation of regional differences and similarities. Selecting on a current issue in management information system the paper provides a detailed amount of knowledge on the possible reasons for the issues such as factors of economic development, technological status, and political/legal environment. The paper concludes by providing a revised framework for the management information issues formulated with effective literature searches. This is going to be effective enough for the studies that will more likely support such reasoning in the future.
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.
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.
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.
Enactment of Firefly Algorithm and Fuzzy C-Means Clustering For Consumer Requ...IRJET Journal
The document proposes a novel methodology for predicting consumer demand and future requests on web pages using a hybrid approach. It first classifies consumers as potential or non-potential using a firefly-based neural network with Levenberg-Marquardt algorithm. Potential consumer data is then clustered using an improved fuzzy C-means clustering algorithm. Finally, upcoming consumer demand is predicted by analyzing patterns and recommending web pages with higher weights. The proposed approach is implemented in Java and CloudSim and aims to overcome limitations of existing recommendation systems by providing more accurate and efficient predictions in shorter time.
IRJET- Enhancing Prediction of User Behavior on the Basic of Web LogsIRJET Journal
The document discusses predicting user behavior based on web logs. It proposes using several algorithms to analyze web log data, including Apriori, KNN, FP-Growth, and an Improved Parallel FP-Growth algorithm. The algorithms are applied to preprocessed web log data to identify frequent patterns and items that provide insights into user behavior. Experimental results show the Improved Parallel FP-Growth algorithm provides higher mining efficiency and can handle large, growing datasets.
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.
This document provides a literature review on methods for predicting user future requests using web usage mining. It discusses several past studies that have used techniques like Markov models, clustering, association rules, and sequential pattern mining to build prediction models from web server log data. The studies aim to reduce user waiting times and server loads by pre-fetching frequently accessed web pages. The document reviews the advantages and disadvantages of different prediction techniques and algorithms discussed in previous research.
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 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.
AN EXTENSIVE LITERATURE SURVEY ON COMPREHENSIVE RESEARCH ACTIVITIES OF WEB US...James Heller
This document summarizes an extensive literature review on web usage mining. It begins by defining web mining and its three types: web content mining, web structure mining, and web usage mining. It then describes the main stages of web usage mining in detail: pre-processing, storage models, pattern discovery, optimization techniques, and pattern analysis. Finally, it discusses the characteristics of web server log data, including that it is unstructured, heterogeneous, distributed, contains different data types and dynamic content, and is voluminous, non-scalable, and incremental in nature.
AN INTELLIGENT OPTIMAL GENETIC MODEL TO INVESTIGATE THE USER USAGE BEHAVIOUR ...ijdkp
The unexpected wide spread use of WWW and dynamically increasing nature of the web creates new
challenges in the web mining since the data in the web inherently unlabelled, incomplete, non linear, and
heterogeneous. The investigation of user usage behaviour on WWW is real time problem which involves
multiple conflicting measures of performance. These measures make not only computational intensive but
also needs to the possibility of be unable to find the exact solution. Unfortunately, the conventional methods
are limited to optimization problems due to the absence of semantic certainty and presence of human
intervention. In handling such data and overcome the limitations of conventional methodologies it is
necessary to use a soft computing model that can work intelligently to attain optimal solution.
This document proposes a new 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 proposed model uses a multi-layered network architecture with backpropagation learning to analyze web log data. Data preprocessing steps like cleaning, user identification, and transaction identification are applied to prepare the enterprise proxy log data for analysis. The proposed framework aims to discover useful patterns from web log data through a combination of K-means clustering and a feedforward neural network.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
A Comparative Study of Recommendation System Using Web Usage Mining Editor IJMTER
Web Mining is one of the Developing field in research. Exact custom of the Web is to get the
beneficial material in the sites. To reduce the work time of user the Web Usage Mining (WUM) technique
is introduced. In this Technique use Web Page recommendation for the Web request from the user. For
the recommendation system in Web Usage Mining (WUM) variousauthor has introduce different
Algorithm and technique to improve the user interest in surfing the Web. Web log files are used todefine
the user interest and there next recommend page to view.The data stored in the web log file consist of
large amount oferoded, incomplete, and unnecessary information. So, the Web log files have to preprocess, customize, and to clean the data. In this paper we will survey different recommendation technique
to identify the issues in web surfing and to improve web usagemining (WUM) pre-processing for pattern
mining and analysis.
1. The document describes a search engine scraper that extracts data from websites, summarizes the extracted information, and converts it into a relevant result for users.
2. The search engine scraper works in three stages: extraction of data from website content, summarization of the extracted data using natural language processing techniques, and conversion of the summarized data into a meaningful format for users.
3. The summarization stage uses natural language toolkit processing libraries to determine sentence similarity, assign weights to sentences, and select sentences with higher ranks to include in the summary.
This document provides an overview of web mining, which involves applying data mining techniques to discover patterns from data on the world wide web. It begins by defining web mining and presenting a taxonomy that distinguishes between web content mining and web usage mining. Web content mining involves discovering information from web sources, while web usage mining involves analyzing user browsing patterns. The document then surveys research on pattern discovery techniques applied to web transactions, analyzing discovered patterns, and architectures for web usage mining systems. It concludes by outlining open research directions in areas like data preprocessing, the mining process, and analyzing mined knowledge.
User Navigation Pattern Prediction from Web Log Data: A SurveyIJMER
This paper proposes a survey of Web Page Prediction Techniques. Prefetching of Web page has been widely used to reduce the access latency problem of the Web users. However, if Prefetching of Web page is not accurate and Prefetched web pages are not visited by the users in their accesses, the limited bandwidth of network and services of server will not be used efficiently and may face the problem of access delay. Therefore, it is critical that we need an effective prediction method during prefetching.
The Markov models have been widely used to predict and analyze users navigational behavior. All the
activities of web users have been saved in web log files. The stored users session is used to extract
popular web navigation paths and predict current users next web page visit.
WebSite Visit Forecasting Using Data Mining TechniquesChandana Napagoda
Data mining is a technique which is used for identifying relationships between various large amounts of data in many areas including scientific research, business planning, traffic analysis, clinical trial data mining etc. This research will be researching applicability of data mining techniques in web site visit prediction domain. Here we will be concentrating on time series regression techniques which will be used to analyse and forecast time dependent data points. Then how those techniques will be applied to forecast web site visits will be explained.
A Review on Pattern Discovery Techniques of Web Usage MiningIJERA Editor
In the recent years with the development of Internet technology the growth of World Wide Web exceeded all expectations. A lot of information is available in different formats and retrieving interesting content has become a very difficult task. One possible approach to solve this problem is Web Usage Mining (WUM), the important application of Web Mining. Extracting the hidden knowledge in the log files of a web server, recognizing various interests of web users, discovering customer behavior while at the site are normally referred as the applications of web usage mining. In this paper we provide an updated focused survey on techniques of web usage mining.
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.
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MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER MULTICLASS STUDENT DATASET
1. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
DOI:10.5121/ijcsa.2016.6401 1
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR
THE SLOW LEARNER PREDICTION OVER
MULTICLASS STUDENT DATASET
Swati1
, Rajinder Kaur2
1
M.E student of Chandigarh University , Gharuan
2
Assist. Professor of Chandigarh University, Gharuan
ABSTRACT
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 effective classification model when evaluated from the
results described in the earlier sections.
KEYWORDS
Slow learner prediction, data classification, averaging factor classification, floating factor classification.
1. INTRODUCTION
Self-organizing Sensor networks dynamically changes the network topology and distributed
either randomly or uniformly. A huge amount of tiny sensor nodes (SNs) monitor temperature,
humidity, motions and sound. In multi-hop transmission of WSN each sensor nodes play dual
characteristic of perceiving the environment and forwards the collected data to the base station
(BS) via integrated radio transmitters. The key challenge is to prolong the lifetime of WSN since
it is not possible to recharge the batteries of SNs in unattended environment. Considering every
node in the network for a time periodic data collection generates more traffic. So the period for
data collection is to be enough for collecting data from nodes. To avoid traffic congestion and
packet drop over transmission only random nodes to be selected for data collection in every
miniature period. Therefore, energy efficient mechanisms are required for computation operations
like data storage, path construction and decision making of source nodes and to secure the
communication from sources to sink.
In the Internet, web is a vast, dynamic, diverse and amorphous data repository that stores
information/data in incredible amount and also enhances the complexity to deal with the
information from different opinion of users, view, and business analyst and web service
providers. The Internet service providers desire to search the technique to guess the user's
behaviors and customize information to shrink the traffic load and create the Web site suited for
2. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
2
different set of users. The business analysts need to have tools to learn consumer's needs. All of
them are expecting equipment or techniques to help them satisfy their requirements and solve the
problems encountered on Web. Thus, Web mining has become trendy active area and is taken as
the research topic for this analysis.
In the prediction model, we find out what users are looking for on internet or Few user so be
might be survey at only documented data. It is the submission of form and facts of mining
techniques to find out interesting usage pattern from World Wide Web facts and figures in the
alignment to realize and better serve the desires of Web based applications. Usage figures and
facts hold the personal or source of World Wide Web users along with browsing at World Wide
Web site. Web usage excavation itself could be categorized farther counting on the kind of usage
facts and figures considered:
• Web Server Data: The client logs are anthologized by the Web server.
1.1 Application Server Data
Financial submission servers have significant characteristics to endue ecommerce
submissions to be built on peak of them with tiny effort. A key feature is thedexterity to
pathway diverse kind of enterprise events and logs them in application server logs.
1.2 Application Level Data
New type of events can be characterized in an application, and logging can be two times on
for them, therefore generating histories of these particularly characterized events. It should be
noted however, that numerous end submissions need a combination of one or more then one
of the methods directed in the classes above.
In the current scenario of the World Wide Web, the popularity is increasing day by day, so as the
web mining. In the web, increasing the number of websites and web users, the data of web usage
is stored on web servers. By analysis of web server data, we have several information such as
user surfing behavior that is most crucial aspect of business marketing which helpsto user profile,
web site designs meliorate and make better marketing and business decision making website user
friendly and popular. With looking at minimum data we cannot identify patterns, for purpose of
analyst need significantly huge amount of data. All the data collected by the service providers is
stored in the high capacity servers. As the user becomes high in the numbers this data also grows
and the data logs are not easy to maintain. To analyze such kind of large data we need to extract
the useful data and this data is then mined to get the patterns of the user behavior. For this
purpose an efficient algorithm is needed, which can do the purpose and help extracting the
information. There are so many algorithms which are liable to resolve the purpose but they all
take the time in scanning and pattern matching. In this synopsis, an algorithm is designed which
employs the website architecture and gives the information about the users’ usage behavior. The
users access a website by going from one page to another page, by the hyperlinks provided in the
web pages. Mining the information identified by the analysis, will not only help making the user
interface better but also in various business decision making.
The user traverses web-site in different-different ways. The variations between traversal patterns
increase the complexity of obtained information from path traversed. There are several available
algorithms for citing the user traversal patterns. In this paper a new approach has been proposed
for mining the large reference patterns. The traversal patterns have achieved by first mining the
maximal forward references take away the web server log and after this maximal forward
reference are obtained and large references can be calculated that are most frequently used paths
followed by user for website.
3. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
3
2. LITERATURE REVIEW
Eltahir, Mirghani, and Anour FA Dafa-Alla [2] have proposedto extract the information from web
server logs using prediction model.The major problem which faces any website admin or web
application system is data increase per-second that is stored in different formats and types in
server log files about users, future needs and maintains structure and apprised of website or web
services according to their preceding data. Prediction model aims at discovering useful
information or learning from usage data registered in log files, based on primary kinds of data
used in the mining process. By using one of the web mining techniques, this paper cause a
prediction model techniques to procure knowledge from web server log files where all user
piloting history is registered. Gupta, Ashika, Rakhi Arora, Ranjana Sikarwar, and Neha Saxena
[3] have projected a technique for prediction model using improved Frequent Pattern Tree
algorithms. Prediction model itself can be categorized further dependsupon usage data considered
are application server, web server and application level data. This Research work target on web
use mining and especially keeps tabs on running crosswise the web utilization examples of sites
from the server log records. The binding of memory and time usage is compared by means of
Apriori algorithm and refined Frequent Pattern Tree algorithm. Sharma, Murli Manohar, and
Anju Bala [4] discussed an algorithm for frequent access pattern identification in prediction
model. In web mining the analysis of web logs is done to identify the user search patterns. In
general approaches of find the patterns, pattern tree is created and the analysis is done, but in
proposed algorithm there is no need of tree creation and the analysis is done based on the website
architecture, which will increase the ability of the other pattern matching algorithms and needs
only one database scan. Bhargav, Anshul, and MunishBhargav [5] have worked on pattern
discovery and users classification through prediction model. The proposed structure is based on
three steps. In the first step, preprocessing is done to remove useless data from web log file so as
to depreciate its size. In the second step, this clean up the log file is used for discovering usage
patterns. For ever, the discovered patterns conduct to the classification of users: on the basis of
countries; on the basis of direct portal to the site or associated by the new site; on the basis of
time of connection , i.e., either different seasons or different months or peculiar days. This
information can then be used by the website administrators for efficient legislation and personal
of their websites and thus the specific needs of express communities of users can be fulfilled and
so the profit can be increased.
3. SIMULATION MODEL
Prediction is making claims about the something that will happen, often based on information
from past and from current state. Everyone has solved their issue of prediction every day with
several degrees of success. For example harvest, weather, energy consumption, movements of
fore x currency pairs or of shares of stocks, earthquakes, and lot of stuff needs to be predicted. In
technical domain predictable of system can be often expressed and evaluated using equations -
prediction is simply evaluation or solution of equations. However, practically face the problems
where such a description would be too complicated or not possible fully. In addition, the solution
by method could be complicated computationally, and sometimes get the solution after event to
be predicted happened. There is possibility to use several approximations, for example regression
of dependency of predicted variable on events which is extrapolated to future. Find such
approximation can be also difficult. This approach is generally means create the model of
predicted event. Neural networks can be used for prediction with several levels of success. The
advantage of automatic learning of dependencies only from measured the data without any need
to add further information. The neural network is trained from the historical data with the hope
that it will be discover hidden dependencies and that it will be able to use them for predicting into
future. In other words, neural network is not represent by an explicitly given model. It is more a
black box that is able to learn something. It is possible to be predict various types of data,
however in the rest of this text we will focus on predicting of time series. Time series show the
4. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
4
development of a value in time. Of course, the value can be impact by also other factors than just
for time. Time series are be represents discrete history of a value and from a continuous function
it can be obtained using sampling.
The Bayesian Classification is represented supervised learning method too a statistical method for
classification. Assumean underlying probabilistic model which allowscapturing uncertainty about
the model in a principle way by determining probabilities of the outcomes. It can solve diagnostic
and predictive problems.
Figure 3.1: Naïve Bayes classification model for slow learner prediction
In this Classification is named after Thomas Bayes (1702-1761), who projected Bayes Theorem.
Bayesian classification offers prior knowledge and practical learning algorithms and observed
data can be combined. explicit probabilities to hypothesis and it is robust to noise in input data.
The slow-learner prediction model is used for purpose of slow learner prediction model using
adaptive classification model. Dependsupon the precise nature of probability model, naive Bayes
classifiers can be trained efficiently in the supervised learning setting. Naive Bayes classifiers
work much better in several complex situations than one might expect. Here independent or
dependent variables are considered for the purpose of the prediction or existence of the crisis. In
spite of their naive design oversimplified assumptions, naive Bayes classifiers often work much
better in more complex real world situations and it is also solve the floating point values
problems . Recently, careful analysis of the Bayesian classification problem has shown the some
theoretical reasons of the apparently unreasonable efficacy of naive Bayes classifiers. An
advantage of naive Bayes classifier is that it required a small amount of training data to estimate
the parameters that are necessary for classification.
5. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
5
Navie Bayes Classifier Algorithm is generic is described as following:
1. Each data sample is represented by an n dimensional element vector, X = (x1, x2….. xn),
depicting n measurements made on the sample from n attributes, respectively A1, A2, An.
2. Suppose that there is m classes, C1, C2……Cm. Given an unknown data sample, X (i.e.,
having no class label), the classifier desire predict that X belongs to the class having the
highest posterior probability, conditioned on X. That is, the naive probability appoint an
unknown sample X to the class Ci.
3. if and only if:
P(Ci/X)>P(Cj/X) for all 1< = j< = m and j!= i
So we maximize P(Ci|X). The class Ci for which P(Ci|X) is maximized is called the
maximum posteriori hypothesis. Beyond Bayes theorem,
P(Ci/X)= (P(X/Ci)P(Ci))/P(X)
As P(X) is constant for all classes, only P(X|Ci)P(Ci) need be maximized. If the class anterior
probabilities are not known, then it is commonly assumed that the classes are same likely, i.e.
P(C1) = P(C2) = …..= P(Cm), and we would therefore maximize P(X|Ci). Otherwise, we
maximize P(X|Ci)P(Ci). Note that the class anterior probabilities may be estimated by P(Ci)
= si/s , where Si is the number of training pattern of class Ci, and s is the total number of
training samples.
Algorithm 1: Naive Bayes Classifier for Slow Learner Prediction
• Computation diagnosis=“yes”, diagnosis=“no” probabilities Pyes, Pno from training
input.
• For Each Test Input Record
• For Each Attribute
• Count Category of Attribute Based On Categorical Division
• Calculate Probabilities Of Diagnosis=“Yes”, Diagnosis=“No” Corresponds To This
Category P(Attr,Yes), P(Attr,No) From Training Input.
• For Each Attribute
• CountResultyes= Resultyes* P(Attr,Yes),Resultno= Resultno*P(Attr,No);
• Calculate Resultyes= Resultyes *Pyes
• Resultno= Resultno*Pno;
• If(Resultyes>Resultno) So Diagnosis=“Yes”;
• Else Then Diagnosis =“No”;
The Formulae used under the Naïve Bayes classifier algorithm:
• Pyes=total number of yes/total no. of records.
• Pno=total number of no/total no.of records.
• P(attr,yes)=total number of yes in corresponding category/entire number of yes.
• P(attr,no)=total number of yes in corresponding category/entire number of yes.
Algorithm 2: Customized Naïve bayes classification model
1. Load the student review dataset
2. Select the parameter set according to the input requirement
3. Select the naïve bayes for the classification model
4. Run the classification model
5. Initiate the iteration parameters
6. For each input record
a. For each attribute
6. International Journal on Computational Science & Applications (IJCSA) Vol.6, No. 4, August 2016
6
i. The entities are divided in the separate categories according the categorical data.
ii. The probability is calculated from the training input
7. For each attributes
a. Calculate the probability and classify the data according the found irrelevance
parametric setup.
b. Return the diagnosis parameters
8. Return the sentiment classification data
4. RESULT ANALYSIS
4.1 Precision:
Precision can be defined as the ratio of related retrieved documents and the information needed
by the users. High precision defines this the algorithm returns results that are relevant as
compared to irrelevant results. It also defines a predictive value that is positive and this is defined
in terms of the binary classification. This classification defines the documents that are retrieved. It
is defined in terms of the results that the system returns at some close-off rank. Precision is also
known as sensitivity.
Precision= A/ (A+D)
Where, A depicts True Positive, B gives the True Negative, C depicts the False Negative and D is
the False Positive.
4.2 Recall
Recall is the probability that a test will indicate ‘test’ amid those with the matching sample.
Recall= A/ (A+C) * 100
4.3 True Positive Rate (TPR)
True positive rate is describe as division of system whichdoes not matches patterns of input
correctly to template that is non matching. It is defined percentage of inputs that are valid. True
positive rate is dependent upon threshold. It is also defined as the measure that an attempt by the
user that is unauthorized will be accepted by the classification calssic.
4.4 False Positive Rate (FPR)
False positive rate is describe as the probability of a system to detect the matching between the
pattern that is given as input and the matching template. It is the fraction of number of false
appearances to the number of attempts that are identified. It defines a measure that an attempt by
the user that is unauthorized is rejected by the classification model.
Table 4.1: The classification instances based analysis
CLASS NUMBER OF INSTANCES PERCENTAGE
Correctly Classified
Instances
10279 92.98%
Incorrectly Classified
Instances
776 7.02%
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The correctly classified and incorrectly classified instanced along with the percentage has been
studied in the table 4.1. The table shows the 10279 samples as the correctly classified instances,
which makes the 92.98% percent of the total instance population, whereas the 776 samples have
been incorrectly classified using the proposed model and it makes the 7.02% of the total
instances.
Figure 4.1: Percentage of classification instances
The figure 4.1 shows the graphical presentation of the table 4.1 in the form of the bar graph. The
left side bar graph shows the higher level of percentage, whereas the incorrect bar in the right side
is showing very low level which shows the marginal value of the incorrectly classified samples
using the naïve bayes classifier over the direct score based evaluation.
Figure 4.2: Number of classification instances
The figure 4.1 shows the graphical presentation of the table 4.1 to depict the instance
classification in the form of number of instances. The left side bar graph shows the higher number
of correctly classified instances, whereas the right side bar shows the incorrect number of
instances using the naïve bayes classifier over the direct score based evaluation.
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Table 4.2: The results of the statistical cross-validation tests
PARAMETER VALUE
Kappa Statistics 0.86%
Mean Absolute Error 0.089%
Root Mean Squared Error 0.23%
Relative Absolute Error 18.12%
Root relative squared error 46.39%
Total Number of Instances 11055
The table 4.2 shows the study of various performance errors, which are calculated using the
statistical measures, which are studied in the form of the statistical errortype 1 and type 2. The
statistical errorshave been utilized to read the various performance errors.
Figure 4.3: Statistics Error Analysis of the classification model
The table 4.3 shows the graphical presentation of the study of various performance errors, which
are calculated using the statistical measures in the above table 4.2. The density of the errors has
been measured in the performance error wheel. The highest error has been recorded for the root
relative squared error, which shows the difference between the multi-methodbased evaluation of
the proposed model results.
Table 4.3: The parametric results obtained from the simulation
PARAMETER CLASS WEIGHTED
AVERAGE-1 1
True Positive Rate 0.904 0.95 0.93
False Positive Rate 0.05 0.096 0.076
Precision 0.936 0.926 0.93
Recall 0.904 0.95 0.93
F-Measure 0.919 0.938 0.93
ROC Area 0.981 0.981 0.981
The table 4.3 shows the study of various performance parameters, which are calculated using the
statistical measures, which are studied in the form of the statistical error type 1 and type 2. The
statistical measures has been utilized to read the various performance parameters.
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Table 4.4: The overall result confusion matrix
CLASS
CLASSIFIED AS
A B
4427 471 A = -1
305 5852 B = 1
The table 4.4 shows the measurement of the number of instances according to the statistical type
1 and type 2 errors, which are calculated using the statistical measures, which are studied in the
form of the statistical error type 1 and type 2. The statistical measures have been utilized to read
the various performance parameters. The proposed model results have been obtained in the form
of various performance parameters.
Figure 4.4: Naïve based classification based upon the student group 1
The results of the first method of the proposed model have been shown in the figure 4.4. The
naïve bayes classification has been performed over the input data using the subject group one.
The subject group one includes the subjects of mathematics and science, which are considered as
the higher order classification of the slow learner students out of the given databases. The most
normalized classification spread has been shown in the figure 4.4 and 4.5. The figure 4.5 shows
the classification results over the subject group 2, which includes the social studies and the
English language. The proposed model has performed almost similar in the case of both of the
subject groups.
Table 4.5: The pre-classification categorization results
Value
Count of
Values Percentage
Normal 1155 88.85
Slow 145 11.15
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The table 4.5 contains the results obtained from one entity (or one school), which contains the
total records of the 1300 students from the 8th
, 9th
and 10th
grades. The earlier stage manual
classification shows the results shown in the table 4.5, where the 88.85% students are considered
as the normal students and the others are estimated as the slow learners.
Figure 4.5: Naïve based classification based upon the student group 2
Table 4.6: The post-classification categorization results after the multiple variance based result evaluation
Category normal Slow
First Stage Classification 88.85 11.15
Subject Class 1 40.8 10.54
Subject Class 2 47.2 13.14
Averaging Factors 44.82 6.3
Floating Factors 11.42 3.71
The 4.6 shows the results obtained from all of the classification stages for the prediction of the
slow learner students out of the given data.
5. CONCLUSION
The proposed model has been programmed to return the results in the form of accuracy and class
density along with the classification obtained from the input dataset. The proposed model has
been designed to detect and classify the input dataset to the given classifier by using the results
obtained from the online student data portal. The experimental results have been obtained from
the simulation model in the form of the enlisted performance parameters. The proposed model
output has been designed in the way to perform all of the operations in the sequential order as per
the system design. The simulation model detects the abnormalities in the given API data. The
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proposed model has been designed to detect and track the slow learners in the given database
which is observed on the basis of the input dataset. The proposed model results show the
effectiveness in the automatic classification of the student data. The proposed model has
performed better for the classification of the student data.
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AUTHOR
Swati is completed the B.Tech in 2014. She is pursuing the Master of Engineering
in the Chandigarh university Gharuan, Kharar from the 2014 to till date. Her
area of interest in the Education data mining is sub area of Data mining.