This document discusses predicting user navigation patterns from web logs. It begins with an introduction to increasing online shopping and the challenge of identifying optimal pages for users. It then reviews the literature on web usage mining and identifying gaps.
The proposed framework has four parts: 1) collecting weblogs of user activity, 2) preprocessing the weblogs to clean, identify users and sessions, 3) modeling navigation patterns as trees of page transitions, and 4) classifying patterns as frequent, semi-frequent, or infrequent using an improved spanning algorithm to analyze user interests. The classification determines if users are interested in purchases based on pattern counts.
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.
Advance Clustering Technique Based on Markov Chain for Predicting Next User M...idescitation
According to the survey India is one of the
leading countries in the word for technical education and
management education. Numbers of students are increasing
day by day by the growth rate of 45% per annum. Advancement
in technology puts special effect on education system. This
helps in upgrading higher education. Some universities and
colleges are using these technologies. Weblog is one of them.
Main aim of this paper is to represent web logs using clustering
technique for predicting next user movement and user
behavior analysis. This paper moves around the web log
clustering technique based on Markov chain results .In this
paper we present an ideal approach to web clustering
(clustering web site users) and predicting their behavior for
next visit. Methodology: For generating effective result approx
14 engineering college web usage data is used and an advance
clustering approach is presenting after optimizing the other
clustering approach.Results: The user behavior is predicted
with the help of the advance clustering approach based on the
FPCM and k-mean. Proposed algorithm is used to mined and
predict user’s preferred paths. To predict the user behavior
existing approaches have been used. But the existing
approaches are not enough because of its reaction towards
noise. Thus with the help of ACM, noise is reduced, provides
more accurate result for predicting the user behavior. Approach
Implementation:The algorithm was implemented in MAT
LAB, DTRG and in Java .The experiment result proves that
this method is very effective in predicting user behavior. The
experimental results have validated the method’s effectiveness
in comparison with some previous studies.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effectiv
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effective classification model when evaluated from the
results described in the earlier sections.
Methodologies on user Behavior Analysis and Future Request Prediction in Web ...ijbuiiir1
Web Usage Mining is a kind of web mining which provides knowledge about user navigation behavior and gets the interesting patterns from web. Web usage mining refers to the mechanical invention and scrutiny of patterns in click stream and linked data treated as a consequence of user interactions with web resources on one or more web sites. Identify the need and interest of the user and its useful for upgrade web Sources. Web site developers they can update their web site according to their attention. In this paper discuss about the different types of Methodologies which has been carried out in previous research work for Discovering User Behavior and Predicting the Future Request.
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
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.
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.
Advance Clustering Technique Based on Markov Chain for Predicting Next User M...idescitation
According to the survey India is one of the
leading countries in the word for technical education and
management education. Numbers of students are increasing
day by day by the growth rate of 45% per annum. Advancement
in technology puts special effect on education system. This
helps in upgrading higher education. Some universities and
colleges are using these technologies. Weblog is one of them.
Main aim of this paper is to represent web logs using clustering
technique for predicting next user movement and user
behavior analysis. This paper moves around the web log
clustering technique based on Markov chain results .In this
paper we present an ideal approach to web clustering
(clustering web site users) and predicting their behavior for
next visit. Methodology: For generating effective result approx
14 engineering college web usage data is used and an advance
clustering approach is presenting after optimizing the other
clustering approach.Results: The user behavior is predicted
with the help of the advance clustering approach based on the
FPCM and k-mean. Proposed algorithm is used to mined and
predict user’s preferred paths. To predict the user behavior
existing approaches have been used. But the existing
approaches are not enough because of its reaction towards
noise. Thus with the help of ACM, noise is reduced, provides
more accurate result for predicting the user behavior. Approach
Implementation:The algorithm was implemented in MAT
LAB, DTRG and in Java .The experiment result proves that
this method is very effective in predicting user behavior. The
experimental results have validated the method’s effectiveness
in comparison with some previous studies.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effectiv
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effective classification model when evaluated from the
results described in the earlier sections.
Methodologies on user Behavior Analysis and Future Request Prediction in Web ...ijbuiiir1
Web Usage Mining is a kind of web mining which provides knowledge about user navigation behavior and gets the interesting patterns from web. Web usage mining refers to the mechanical invention and scrutiny of patterns in click stream and linked data treated as a consequence of user interactions with web resources on one or more web sites. Identify the need and interest of the user and its useful for upgrade web Sources. Web site developers they can update their web site according to their attention. In this paper discuss about the different types of Methodologies which has been carried out in previous research work for Discovering User Behavior and Predicting the Future Request.
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
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.
Classification of User & Pattern discovery in WUM: A SurveyIRJET Journal
This document summarizes research on web usage mining techniques. It discusses how web usage mining involves discovering patterns from web server logs to understand how users interact with websites. The document reviews several papers on preprocessing log data, pattern discovery methods like clustering and classification, and classifying users based on patterns. It also provides an overview of the web usage mining process, which typically involves preprocessing, pattern discovery from cleaned logs, and using patterns to classify users. The goal is to help website administrators better understand users and personalize websites.
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
Automatic recommendation for online users using web usage miningIJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Automatic Recommendation for Online Users Using Web Usage Mining IJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
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.
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.
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.
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.
The document describes a proposed fuzzy logic-based model for classifying web users in a personalized search system. The model collects user browsing data using a customized browser. It then fuzzifies the data and generates fuzzy rules using decision trees. These rules are used to label search pages and group users according to their search interests. The model is evaluated against a Bayesian classifier and shown to perform better. The goal is to handle the dynamic and fluctuating nature of user behavior and interests that exist in a personalized web search environment.
an approach to recommend pages to user after path completionIJAEMSJORNAL
This document proposes a recommendation system that recommends web pages to users based on analyzing user browsing activity and completing partial browsing paths. It involves the following steps:
1. Preprocessing log data which includes cleaning, identifying user sessions and paths, and completing partial paths.
2. Clustering user sessions into groups with similar browsing patterns using k-means clustering.
3. Creating user profiles for each cluster based on frequently accessed pages.
4. Checking the similarity of new user paths to generated profiles and recommending pages that are similar above a threshold to improve browsing performance.
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.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines.
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.
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.
IRJET- Text-based Domain and Image Categorization of Google Search Engine usi...IRJET Journal
This document discusses a proposed system for categorizing search engine results using conceptual clustering. The system analyzes the content of search results to extract relevant concepts, then uses a personalized conceptual clustering algorithm to generate a decision tree of query clusters. This tree can be used to identify categories for web pages and provide topically relevant results to users. The system aims to improve on traditional ranked search results by categorizing results based on the conceptual preferences and interests of individual users.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
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.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
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.
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
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Classification of User & Pattern discovery in WUM: A SurveyIRJET Journal
This document summarizes research on web usage mining techniques. It discusses how web usage mining involves discovering patterns from web server logs to understand how users interact with websites. The document reviews several papers on preprocessing log data, pattern discovery methods like clustering and classification, and classifying users based on patterns. It also provides an overview of the web usage mining process, which typically involves preprocessing, pattern discovery from cleaned logs, and using patterns to classify users. The goal is to help website administrators better understand users and personalize websites.
An effective search on web log from most popular downloaded contentijdpsjournal
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void
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proposed conceptual prediction model automatically generates a semantic network of the semantic Web
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discovered knowledge can then be used for many practical web applications such as web
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Automatic recommendation for online users using web usage miningIJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Automatic Recommendation for Online Users Using Web Usage Mining IJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
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.
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.
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.
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.
The document describes a proposed fuzzy logic-based model for classifying web users in a personalized search system. The model collects user browsing data using a customized browser. It then fuzzifies the data and generates fuzzy rules using decision trees. These rules are used to label search pages and group users according to their search interests. The model is evaluated against a Bayesian classifier and shown to perform better. The goal is to handle the dynamic and fluctuating nature of user behavior and interests that exist in a personalized web search environment.
an approach to recommend pages to user after path completionIJAEMSJORNAL
This document proposes a recommendation system that recommends web pages to users based on analyzing user browsing activity and completing partial browsing paths. It involves the following steps:
1. Preprocessing log data which includes cleaning, identifying user sessions and paths, and completing partial paths.
2. Clustering user sessions into groups with similar browsing patterns using k-means clustering.
3. Creating user profiles for each cluster based on frequently accessed pages.
4. Checking the similarity of new user paths to generated profiles and recommending pages that are similar above a threshold to improve browsing performance.
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.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines.
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.
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.
IRJET- Text-based Domain and Image Categorization of Google Search Engine usi...IRJET Journal
This document discusses a proposed system for categorizing search engine results using conceptual clustering. The system analyzes the content of search results to extract relevant concepts, then uses a personalized conceptual clustering algorithm to generate a decision tree of query clusters. This tree can be used to identify categories for web pages and provide topically relevant results to users. The system aims to improve on traditional ranked search results by categorizing results based on the conceptual preferences and interests of individual users.
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
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.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
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.
Similar to Predicting the user navigation pattern from web logs using weighted support approach (20)
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
Control of a servo-hydraulic system utilizing an extended wavelet functional ...nooriasukmaningtyas
Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
Decentralised optimal deployment of mobile underwater sensors for covering la...nooriasukmaningtyas
This paper presents the problem of sensing coverage of layers of the ocean in three dimensional underwater environments. We propose distributed control laws to drive mobile underwater sensors to optimally cover a given confined layer of the ocean. By applying this algorithm at first the mobile underwater sensors adjust their depth to the specified depth. Then, they make a triangular grid across a given area. Afterwards, they randomly move to spread across the given grid. These control laws only rely on local information also they are easily implemented and computationally effective as they use some easy consensus rules. The feature of exchanging information just among neighbouring mobile sensors keeps the information exchange minimum in the whole networks and makes this algorithm practicable option for undersea. The efficiency of the presented control laws is confirmed via mathematical proof and numerical simulations.
Evaluation quality of service for internet of things based on fuzzy logic: a ...nooriasukmaningtyas
The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Smart monitoring system using NodeMCU for maintenance of production machinesnooriasukmaningtyas
Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.
Design and simulation of a software defined networkingenabled smart switch, f...nooriasukmaningtyas
Using sustainable energy is the future of our planet earth, this became not only economically efficient but also a necessity for the preservation of life on earth. Because of such necessity, smart grids became a very important issue to be researched. Many literatures discussed this topic and with the development of internet of things (IoT) and smart sensors, smart grids are developed even further. On the other hand, software defined networking is a technology that separates the control plane from the data plan of the network. It centralizes the management and the orchestration of the network tasks by using a network controller. The network controller is the heart of the SDN-enabled network, and it can control other networking devices using software defined networking (SDN) protocols such as OpenFlow. A smart switching mechanism called (SDN-smgrid-sw) for the smart grid will be modeled and controlled using SDN. We modeled the environment that interact with the sensors, for the sun and the wind elements. The Algorithm is modeled and programmed for smart efficient power sharing that is managed centrally and monitored using SDN controller. Also, all if the smart grid elements (power sources) are connected to the IP network using IoT protocols.
Efficient wireless power transmission to remote the sensor in restenosis coro...nooriasukmaningtyas
In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Grid reactive voltage regulation and cost optimization for electric vehicle p...nooriasukmaningtyas
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
Topology network effects for double synchronized switch harvesting circuit on...nooriasukmaningtyas
Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the double synchronized switch harvesting (DSSH). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, mono DSSH, and DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results show that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSHind by 240%.
Improving the design of super-lift Luo converter using hybrid switching capac...nooriasukmaningtyas
In this article, an improvement to the positive output super-lift Luo converter (POSLC) has been proposed to get high gain at a low duty cycle. Also, reduce the stress on the switch and diodes, reduce the current through the inductors to reduce loss, and increase efficiency. Using a hybrid switch unit composed of four inductors and two capacitors it is replaced by the main inductor in the elementary circuit. It’s charged in parallel with the same input voltage and discharged in series. The output voltage is increased according to the number of components. The gain equation is modeled. The boundary condition between continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been derived. Passive components are designed to get high output voltage (8 times at D=0.5) and low ripple about (0.004). The circuit is simulated and analyzed using MATLAB/Simulink. Maximum power point tracker (MPPT) controls the converter to provide the most interest from solar energy.
Third harmonic current minimization using third harmonic blocking transformernooriasukmaningtyas
Zero sequence blocking transformers (ZSBTs) are used to suppress third harmonic currents in 3-phase systems. Three-phase systems where singlephase loading is present, there is every chance that the load is not balanced. If there is zero-sequence current due to unequal load current, then the ZSBT will impose high impedance and the supply voltage at the load end will be varied which is not desired. This paper presents Third harmonic blocking transformer (THBT) which suppresses only higher harmonic zero sequences. The constructional features using all windings in single-core and construction using three single-phase transformers explained. The paper discusses the constructional features, full details of circuit usage, design considerations, and simulation results for different supply and load conditions. A comparison of THBT with ZSBT is made with simulation results by considering four different cases
Power quality improvement of distribution systems asymmetry caused by power d...nooriasukmaningtyas
With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Renewable energy based dynamic tariff system for domestic load managementnooriasukmaningtyas
To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
Energy harvesting maximization by integration of distributed generation based...nooriasukmaningtyas
The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
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Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
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openshift technical overview - Flow of openshift containerisatoin
Predicting the user navigation pattern from web logs using weighted support approach
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 21, No.3, March 2021, pp. 1722~1730
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i3.pp1722-1730 1722
Journal homepage: http://ijeecs.iaescore.com
Predicting the user navigation pattern from web logs using
weighted support approach
Om Prakash P.G1
, Jaya A2
, Ananthakumaran S3
, Ganesh G4
1,3
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur,
Andhra Pradesh, India
2
Department of Computer Applications, B.S.A.Crescent Institute of Science and Technology, Chennai, Tamilnadu, India
4
Department of Computer Science, BlueCrest College, Freetown, Sierra Leone
Article Info ABSTRACT
Article history:
Received Mar 13, 2020
Revised Sep 7, 2020
Accepted Sep 18, 2020
A weblog contains the history of previous user navigation pattern. If the
customer accesses any portal of organization website, the log is generated in
web server, based on sequence of user transaction. The weblog stored in the
web server as unstructured format, it contains both positive and negative
responses i.e. successful and unsuccessful responses, identifying the positive
and negative response is not useful for identifying user behavior of individual
user. Initially the successful response is taken, from that conversion of
unstructured log format to structured log format through data preprocessing
technique. The process of data preprocessor contains three step process data
cleaning, user identification and session identification. The pattern is
discovered by preprocessing technique from that user navigation pattern is
generated. From that navigation pattern classifier technique is applied, the
conversion of sequence pattern to sub sequence pattern by clustering
technique. This research is to identify the user navigation pattern from
weblog. The Improved Spanning classification algorithm classifies the
frequent, infrequent and semi frequent pattern. To identify the optimal
webpage using classificatopn algorithm from thet user behavior is identified.
Keywords:
Classification
Mining
Prediction
User behavior
User navigation
Web mining
Web traversal
Weblog
This is an open access article under the CC BY-SA license.
Corresponding Author:
Om Prakash P.G
Department of Computer Science and Engineering
Koneru Lakshmaiah Education Foundation, Vijayawada
Andra Pradesg, Inida
Email: ommail2004@gmail.com
1. INTRODUCTION
Now a day lot of products available in online websites, so the number of users is increased due to
attraction of price discounts in online shopping. In the current situation, the percentage of users is increased
day by day to access the internet. The users are utilizing the internet lot of time to access the websites; the lot
of products offers is available in websites. So more number of user shown interest in online shopping.
To identify the user’s optimal page is a difficult task in online shopping. While the users accessing
the website, the user searching transactions are stored as log format. Initially, the log as unstructured format,
the conversion of unstructured log to structured information by data pre-processing technique. The weblog
consists of various entries like Logical address of user, date & time stamp, categories of product details and
status code of the webpage. The pre-processing is a three-step process. The data cleaning contains relevant
and irrelevant data; it cleans the irrelevant data in log. The user identification is identified by IP address of
the specified user that is stored in the weblog. The session identification is classified based on session
management of each user accessing the websites.
2. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Predicting the user navigation pattern from web logs using weighted support approach (Om Prakash P.G)
1723
Every organization identifies the user’s demand, so organization will manufacture depends upon users
need. If the product is currently not available to the user, the user may purchase some other product that is
available in the online websites. If the product is crossed benchmark production, the sale of product to the
customer is a biggest challenge, so the organization identifies the need the demand of the user. The calculation
of identify the demand is different for each product. So the organization has to concentrate the need of the users
demand. With the help of weblog the demand of each of the individual user needs are monitored.
The process of identifying user needs and user interest is a difficult task. With the help of weblog
the user needs and interest of every user can identify by user navigation pattern. The user navigation path
pattern can be analyzed by history of pattern that is taken from weblog. Using pattern discovery is the
process of convert sequence in to sub sequence of similar pattern. By using forward path and backward path
technique subsequence pattern can be generated. Then the sub sequence pattern is grouped to form a cluster,
that will helpful to identify the number of user needs. The user interest is a biggest task of every organization.
By using improved span classification algorithm the user interest will classified.
The paper is organized as follows, Section 2 is deals review of literature, Section 3 deals with
problem definitions, Section 4 describes framework for user navigation system, and Section 5 gives the
results & discussion. Finally Section 6 concludes the paper.
2. LITERATURE SURVEY
WUM is a mining technique that is to identify the user pattern from that navigational behaviour is
analysed that was stored in weblog. Wangshu et al. [1] showed that preprocessing has two stages, first it
eliminates unwanted and similar datasets and then it removes irrelevant features of ranking. Xin et al. [2]
showed that (OSN) classified in to extroversive and introversivebehavior. First, it identifies how users
communicate with online friends then it gathering social information in the following websites.
Guoshuai et al. [3] showed that prediction service is based on rating. Ruili et al. [4] suggested that
user behaviour pattern, it analyzesth of actual usage and anticipated usage. The actual usage is get transaction
database based on sessions. The anticipated usage has to get the session from the count of user path is
identified. Surbhi et al. [5] showed that back navigation approach utilizes forward path and backward path to
identify frequent pattern, semi frequent pattern and infrequent pattern. Zliobaite et al [6] identified the
prediction accuracy by adaptive pre-processing algorithm. Om Prakash et al. [7] suggested user pattern is
classified by navigation behaviour. User behavior can be analyzed based on the count of similar data. If the
count of user is above the benchmark level then the user can be considered as interested user (IU), if it is
below the level then it is not interested user (NIU) from that user behavior is analyzed.
Dr. A. R. Patel et al. [8] identified initially weblog initially contains raw log, so conversion of raw
data to processed information by using pre-processing technique, the data pre-processing has the following
process of cleaning the data then identify the user based on ip address and by using session. Hong Cheng et
al. [9] showed that the traversal pattern, it contains sequence of pattern. To convert the sequence of pattern in
to sub sequence pattern based on navigation pattern modeling. The classification algorithm has there
sequences of pattern generation, it has frequent, semi-frequent and in-frequent. Kerana et al. [10] identified
that, the traversal pattern utilizes database transaction, the sequence of similar and dissimilar transactions are
converted into sub sequence by reducing number of similar transactions to form clusters, through the
clustering technique it classifies frequent, semi frequent and in frequent item data sets, from that user buying
behavior is identified.
Mobasher et al. [11] identified the personalize of each user. The unprocessed weblogs are converted
into processed log by using data pre-processing. After pre-processing the sequence of pattern is generated by
using traversal pattern, it uses forward path the sequence of path is breakdown the sequence in to sub
sequence path. The clustering algorithm reduces the number of similar path grouped to form a cluster. The
classification classifies the user interested pattern and user not interested pattern. Rajesh et al. [12] showed
the three main classification models are random forest, SVM and K-neighbors classifiers, by using single
classifier the prediction accuracy is 30%, while using all the three classifier the prediction accuracy is
increased up to 51%. Vinothkanna et al. [13] suggested to analysis the future detection RBFN classifier is
used for effective classification, RBFN is a good classification and its recognition rate is nearly 99.2%.
Kousar Nikhathet al. [14] discussed text clustering approach along with distance-based approach
model helps in optimizing the text documents. Babu et al. [15] suggested rapid miner tool is a data mining
technique; it helps to cluster the information to get high profit and less risk to customers, that yields profit to the
customers. Gummadi et al. [16] proposed the algorithm to improve the performance of the system using
clustering technique. The clustering technique algorithm uses the LEACH, CLAEER and mean shifted
algorithm to improve the accuracy of energy efficient routing in vehicle tracking using wireless sensor
networks. Karthikeyan et al. [17] suggested neighbor based cluster location aware routing (NCLAR) helps to
3. ISSN:2502-4752
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achieve more packet delivery rate with high location accuracy. Initially cluster region has greater signal strength
with less signal delay, then routing table is constructed is formed with less signal delay value and more signal
strength. In second phase, neighbor node routing table is constructed and updated with addition of more fields.
Bommadevara et al. [18] identified K-means algorithm is suitable for predicting the route cause for
getting the Heart disease. Heart disease prediction is used to determine the root cause of getting heart attack
and the probability of getting a heart attack. Akhila et al. [19] Suggested frequent pattern mining and cluster
analysis, measure and analysis of energy utilization of human activity, to predict human health through smart
devices. Angel Prathyusha et al. [20] utilized Hybrid soft decision model is a classification techniques, the
classifier predict the farmer growth in agriculture development. It helps to cultivate the best suitable crops
rather than unsuitable crops, by using this technique it attained 92% of accuracy. Anjali Devi et al. [21]
utilizes apriori algorithm, it utilizes previous academic information from that to predict the placement of the
current student. Arshad et al. [22] proposed the machine learning based attack detection model is best than
the traditional statistical based learning model and rule based learning models in terms of time, detection rate
are concerned. Arvind Selwal et al. [23] suggested Machine Learning Model, it classify seven to eleven
features, by this model it predict thyroid nearly 99.8%. Chanintorn et al. [24] utilized regression model, it
will forcast the with optimal solutions. Wahab et al. [25] analysed prediction of energy consumption of two
laboratories by using artificial neural network algorithm.
Research GAP:
a) There exists a difficulty in capturing the individual user behaviour.
b) Visitors face difficult to find the relevant web pages that leads to wastage of time.
c) Need to reduce the mining time while capturing the user behavior from weblogs.
d) There is a lack of prediction accuracy to analyze user behavior.
e) User behavior is difficult to analyze the unstructured data.
3. PROPOSED FRAMEWORK FOR USER NAVIGATION SYSTEM
Figure 1 shows the framework for user navigation system, to identify the user behavior, the
following techniques are applied, it consistes of preprocessor, navigation pattern modeling and classification
technique. The role of preprocessor converts the raw log to processed information. The Navigation pattern
modeling converts the above sequence to subsequences. The classification algorithm classifies the above
sequence into frequent sequence and infrequent sequence.
Figure 1. Framework for user navigation system
3.1. Weblogs
Whenever the user accesses the web site, the log is recorded in the form of IP address, categories,
date & time, bytes transmission and status code of each webpage. The log consists of following user
attributes
a) IP address
b) Date and time
c) Request method
d) URL of the page
e) Categories
f) Bytes transmission
g) Status code
Figure 2 shows the structure of weblog, the log consists of Ip address, date and time, request
method, URL of page, number of bytes transmission and status code for the webpage.
4. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Predicting the user navigation pattern from web logs using weighted support approach (Om Prakash P.G)
1725
Figure 2. Weblog structure
3.2. Preprocessing
In pre-processing, it has unstructured data format of user transaction. The pre-processing contains
three step processes
a) Data cleaning
b) User identification
c) Session identification
3.2.1. Data cleaning
These logs have been taken from the vehicle web server, the logs taken from the period of
23/Oct/2016 to 30/Oct/2016 The log file has 3568 data sets, in that each data set contains the IP address,
Categories and status code. The data cleaning will take care of irrelevant information when the user was
visited sited earlier. It cleans the incomplete information that is available in websites. The status code is
generated whenever user transforms to another page. The status code helps to clean the irrelevant information
in log. After the data cleaning process in log 1568 records are obtained.
E.g.: from Figure 2 shows “172.16.2.7- [23/Oct/2016-0600] "GET 9.gif HTTP/1.0" 200 441” the stats code
200-400 is successful webpage of the user.
3.2.2. User identification
The user is identified from cleaning process, it has Users IP address, request time, requested URL,
date & time.
E.g.: Figure 2 shows the user IP address 172.16.2.7 is identified from datasets.
3.2.3. Session identification
It classifies the user based on time sessions, if the similar user visits the website two times, so two
sessions must be recorded.
E.g.: Figure 2 identified the similar ip address of different time stamp from 13:41:41 to 13:45:46 & 14:05:31
to 14:08:54 is considered as two session.
3.3. Navigation pattern modelling
The Table 1 has the browsing pattern of the users. Figure 3 shows the tree structure of navigation
pattern modeling, from the above weblog the tree structure is generated, T1 consider as Home page, T2 as
City selection, T3 as Two wheeler, T4 as LMV, T5 as Heavy Vehicle, T6 as Petrol, T7 as Diesel, T8 as TVS,
T9 as Honda, T10 as Bajaj, T11 as Enfield, T12 as Maruti suzuki, T13 as Hyundai, T14 as OPEL, T15 as
VAN, T16 as Saab, T17 as Bus, T18 as Manual, T19 as Automatic, T20 as Color Selection, T21 as Payment
Gateway, T22 as Credit Card, T23 as Debit Card, T24 as Cash, T25 as Loan option.
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Table 1. User pattern
Pattern Nos Pattern
1 < T1,T2,T3,T5,T6,T8,T18,T20,T21,T22>
2 <T1,T2,T3,T5,T6,T10,T18,T21,T22,T23>
3 <T1,T2,T5,T7,T17,T7,T16>
4 <T1,T2,T4,T5,T7,T16,T19,T20,T21,T25>
5 <T1,T2,T4,T5,T7,T15,T19,T20,T21,T24>
6 <T1,T2,T3,T5,T7,T11,T6,T9>
7 <T1,T2,T4,T5,T6,T4,T7>
T1
T2
T3 T4 T5
T6 T7
T8 T9 T11 T12 T13 T14 T15 T16 T17
T10
T18 T19
T20
T21
T22 T23 T24 T25
Figure 3. Tree structure of navigation pattern modelling
3.4. Classification
Using Improved Spanning algorithm, the pattern is converted in to FS, SFS and IFS. It classifies the
pattern by count. The pattern count is above the threshold then it has IU, so users are interested in purchase,
then user as NIU, then the user not interested in purchase. Table 2 shows the count of user pattern. Table 3
shows the buying interest of vehicle for the individual user. If the count of the pattern determines the Buying
interest of the vehicle.
Table 2. Pattern count
Pattern Nos Pattern Count
1 < T1,T2,T3,T5,T6,T8,T18,T20,T21,T22> 16
2 <T1,T2,T4,T5,T7,T15,T19,T20,T21,T24> 17
3 <T1,T2,T4,T5,T7,T16,T19,T20,T21,T25> 11
4 <T1,T2,T3,T5,T6,T10,T18,T20,T21,T23> 16
5 <T1,T2,T5,T5,T7,T17,T7,T16> 2
Table 3. Interested and not interested users
Pattern Nos Pattern Interested/Not Interested
1 < T1,T2,T3,T5,T6,T8,T18,T20,T21,T22> Interested
2 <T1,T2,T4,T5,T7,T15,T19,T20,T21,T24> Interested
3 <T1,T2,T4,T5,T7,T16,T19,T20,T21,T25> Interested
4 <T1,T2,T3,T5,T6,T10,T18,T20,T21,T23> Interested
5 <T1,T2,T5,T7,T17,T7,T16> Not Interested
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Predicting the user navigation pattern from web logs using weighted support approach (Om Prakash P.G)
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Improved Spanning Algorithm
(Sequential Pattern, FS, SFS, IFS)
{
Step 1: Get the Input Sequence Pattern α
Step 2: Scan the Sequence item b with α
Step 3: for each b with α
Step 4: if item b greater than max_sup
Step 5: Push item b in FS “Frequent Sequence” // end of if
Step 6: else if item b greater than min_sup
Step 7: Push item b in SFS “Semi Frequent Sequence” // end of else if
Step 8: else Push item b in IFS “In Frequent Sequence” // end of else
Step 9: End for
} //end of algorithm
4. RESULTS AND DISCUSSION
The experimentation will be performed using vehicle data. The implementation will be done using
Weka tool and the performance will be compared with the existing algorithms based on precision, recall and
F-measure. Figure 4 shows the Vehicle compactness, Figure 5 shows Circularity of vehicle, Figure 6. Shows
the Categories of individual vehicles and Figure 7. Shows the Different class of vehicles. The proposed
method is implemented using improved spanning algorithm with a knowledge base which has 1568 instances
in log. The maximal is 119, mean is 93.678, Std.Dev is 8.234 and the minimum compactness is 73. In total
19 attributes were generated. The accuracy is calculated by the number of instances generated by the system
and the total number of attributes generated. The IU and NIU can be classified based on the threshold of the
system. Figures 4-7 shows the sample output screen for search result.
Figure 4. Vehicle compactness Figure 5. Circularity of vehicle
Figure 6. Categories of individual vehicles Figure 7. Different class of vehicles
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The Figure 8 shows the buying vehicle prediction in various classes of people and compare the
attributes such as fuel, color, brand, cost, technology and compact, the user has compared with the above
maximum attributes from that prediction is identified. The Figure 9 shows the comparison of existing and
proposed methods. The graph analysis method has precision, recall, and F-measure, the modified improved
span algorithm values are 0.8742, 0.8534, and 0.8775 respectively.
Figure 8. Vehicle prediction
Figure 9. Comparison graphs of existing and proposed algorithm
The comparative discussion Table 4 shows the comparison of existing and proposed methods of
predicting the buying behavior of vehicle. The analysis is compared with existing and proposed method. The
precision, recall, and F-measure of the modified improved span method are 0.8742, 0.8534, and 0.8775,
respectively, that is greater than the existing methods. The precision, recall, and F-measure of existing
methods of prefix span are 0.7903, 0.7396, and 0.6784 respectively.
Table 4. Comparative discussion
Datasets Methods Precision Recall F-measure Prediction accuracy
Vehicle Dataset Prefix Span Algorithm 0.7903 0.7396 0.6784 0.5934
Improved Spanning Algorithm 0.8242 0.8534 0.8775 0.7375
5. CONCLUSION
The proposed method is implemented by using improved spanning classification algorithm, the
proposed algorithm is implemented for vehicle prediction for that weka tool is used. The system will show
the prediction level of user based on attributes such as fuel, cost, technology, compact, color and brand from
that user buying interest is generated and improves prediction accuracy. The prediction accuracy of each user
can be identified by the behaviour of user path that is taken from weblog, based on that IU and NIU can be
classified by modified improved span classification algorithm. The system will focus to increase the buying
prediction behavior from weblog.
0
5
10
15
20
25
30
Vehicle Prediction
Vehicle Prediction
25
20
20
15
10
10
Fuel
Cost
Technology
Compact
Color
Brand
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
q1 q2 q3 q4
Number of queries
Proposed
0
0.2
0.4
0.6
0.8
1
q1 q2 q3 q4
Number of queries
Proposed
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BIOGRAPHIES OF AUTHORS
P.G. Om Prakash is working as an Assistant Professor in the Dept. of Computer Science and
Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra
Pradesh, India. He has Pursuing his Ph.D., degree in Computer Science and Engineering from
B.S.A. Crescent Institute of Science and Technology, Chennai, Tamilnadu, India. He has more
than 9 years of academic and 3 years of Software experience. His recent research interest
includes Web Mining, Cloud Computing, Computer Networks.
9. ISSN:2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 21, No. 3, March 2021 : 1722 - 1730
1730
A. Jaya is working as a Professor in the dept of Computer Applications, B.S.A. Crescent
Institute of Science and Technology, Chennai, Tamilnadu, India. She has received her Ph.D
degree inInformation and Communication Engineering from Anna University, Chennai,
Tamilnadu, India in 2011. She is having 23 years of Academic Experience. Area of Research
NLP, Knowledge Engineering, Web Mining and Text Mining.
S. Ananthakumaran is working as an Associate Professor in the Dept. of Computer Science
and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra
Pradesh, India. He has received his Ph.D., degree in Computer Science and Engineering from
Manonmaniam Sundaranar University, Tamilnadu, India in 2016. He has more than 17 years of
academic and 9 years of research experience. His recent research interest includes Secure Data
Communication, Blockchain Technology, IoT and Deep Learning.
G. Ganesh is working as an Assistant Professor in the Dept. of Computer Science in Blue Crest
College, Freetown, Sierra Leone. He has Completed his M.Tech in SRM University, Chennai,
Tamilnadu, India. He has more than 11 years of academic and 2 years of Software experience.
His recent research interest includes Data Mining, Web Mining, Cloud Computing, Computer
Networks.