Prediction of web user behavior is the demand of today competitive edge of World Wide Web. Predicting the
next web page is not sufficient, evaluation of prediction models is important because every model have its own pros and cons. Prediction results will be helpful if high prediction accuracy is achieved with minimum complexity, which are depended on the prediction model. Various models and their variations are proposed for predicting the next web page accessed by the web user. Markov model and their variations are found suitable for web prediction. In this research we have evaluated and compared various models for predicting next web page accessed by the web user. Experiments are conducted on three different real datasets.
Integrating vague association mining with markov modelijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.
Automatically inferring structure correlated variable set for concurrent atom...ijseajournal
The at
omicity of correlated variables is
quite tedious and error prone for programmers to explicitly infer
and specify in
the multi
-
threaded program
. Researchers have studied the automatic discovery of atomic set
programmers intended, such as by frequent itemset
mining and rules
-
based filter. However, due to the
lack
of inspection of inner structure
,
some
implicit sematics
independent
variables intended by user are
mistakenly
classified to be correlated
. In this paper, we present a
novel
simplification method for
program
dependency graph
and the corresponding graph
mining approach to detect the set of variables correlated
by logical structures in the source code. This approach formulates the automatic inference of the correlated
variables as mining frequent subgra
ph of
the simplified
data and control flow dependency graph. The
presented simplified
graph representation of program dependency is not only robust for coding style’s
varieties, but also essential to recognize the logical correlation. We implemented our me
thod and
compared it with previous methods on the open source programs’ repositories. The experiment results
show that our method has less false positive rate than previous methods in the development initial stage. It
is concluded that our presented method
can provide programmers in the development with the sufficient
precise correlated variable set for checking atomicity
TEXT ADVERTISEMENTS ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKSijdms
In this paper, we describe the developed model of the Convolutional Neural Networks CNN to a
classification of advertisements. The developed method has been tested on both texts (Arabic and Slovak
texts).The advertisements are chosen on a classified advertisements websites as short texts. We evolved a
modified model of the CNN, we have implemented it and developed next modifications. We studied their
influence on the performing activity of the proposed network. The result is a functional model of the
network and its implementation in Java and Python. And analysis of model results using different
parameters for the network and input data. The results on experiments data show that the developed model
of CNN is useful in the domains of Arabic and Slovak short texts, mainly for some classification of
advertisements. This paper gives complete guidelines for authors submitting papers for the AIRCC
Journals.
Studies towards heterogeneous Mobile Adhoc Network (MANET) as well as inter-domain routing is still in much infancy stage. After reviewing the existing literaturs, it was found that problems associated with scalability, interoperability, and security is not defined up to the mark as it should be part of pervasive computing in future networks. Moreover, it was found that existing studies do not consider the complexities associated with heterogeneous MANET to a large extent leading to narrowed research scope. Hence, this paper introduces a novel scheme called as Secure, Scalable and Interoperable (SSI )routing, where a joint algorithm is designed, developed, and implemented. The outcome exhibits the correctness of this scheme by simulation assisted by analysis for inter-domain routing.
The document describes an agent-based prototype for freight train traffic management between Milano and La Spezia stations in Italy. The prototype was developed using CaseLP, an environment for developing multi-agent system prototypes based on logic programming. The prototype models the complex interactions between various entities involved in freight train scheduling and aims to provide decision support for traffic coordinators. Key aspects of the prototype include defining agent classes and behaviors to represent different system actors like terminals and coordinators, and using agent communication to model coordination between agents. The prototype was tested to evaluate the benefits of an agent-based approach for decision support in freight train traffic management.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Dependency analysis is a technique to detect dependencies between tasks that prevent these tasks from running in parallel. It is an important aspect of parallel programming tools. Dependency analysis techniques are used to determine how much of the code is parallelizable. Literature shows that number of data dependence test has been proposed for parallelizing loops in case of arrays with linear subscripts, however less work has been done for arrays with nonlinear subscripts. GCD test, Banerjee method, Omega test, I-test dependence decision algorithms are used for one-dimensional arrays under constant or variable bounds. However, these approaches perform well only for nested loop with linear array subscripts. The Quadratic programming (QP) test, polynomial variable interval (PVI) test, Range test are typical techniques for nonlinear subscripts. The paper presents survey of these different data dependence analysis tests.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
Integrating vague association mining with markov modelijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.
Automatically inferring structure correlated variable set for concurrent atom...ijseajournal
The at
omicity of correlated variables is
quite tedious and error prone for programmers to explicitly infer
and specify in
the multi
-
threaded program
. Researchers have studied the automatic discovery of atomic set
programmers intended, such as by frequent itemset
mining and rules
-
based filter. However, due to the
lack
of inspection of inner structure
,
some
implicit sematics
independent
variables intended by user are
mistakenly
classified to be correlated
. In this paper, we present a
novel
simplification method for
program
dependency graph
and the corresponding graph
mining approach to detect the set of variables correlated
by logical structures in the source code. This approach formulates the automatic inference of the correlated
variables as mining frequent subgra
ph of
the simplified
data and control flow dependency graph. The
presented simplified
graph representation of program dependency is not only robust for coding style’s
varieties, but also essential to recognize the logical correlation. We implemented our me
thod and
compared it with previous methods on the open source programs’ repositories. The experiment results
show that our method has less false positive rate than previous methods in the development initial stage. It
is concluded that our presented method
can provide programmers in the development with the sufficient
precise correlated variable set for checking atomicity
TEXT ADVERTISEMENTS ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKSijdms
In this paper, we describe the developed model of the Convolutional Neural Networks CNN to a
classification of advertisements. The developed method has been tested on both texts (Arabic and Slovak
texts).The advertisements are chosen on a classified advertisements websites as short texts. We evolved a
modified model of the CNN, we have implemented it and developed next modifications. We studied their
influence on the performing activity of the proposed network. The result is a functional model of the
network and its implementation in Java and Python. And analysis of model results using different
parameters for the network and input data. The results on experiments data show that the developed model
of CNN is useful in the domains of Arabic and Slovak short texts, mainly for some classification of
advertisements. This paper gives complete guidelines for authors submitting papers for the AIRCC
Journals.
Studies towards heterogeneous Mobile Adhoc Network (MANET) as well as inter-domain routing is still in much infancy stage. After reviewing the existing literaturs, it was found that problems associated with scalability, interoperability, and security is not defined up to the mark as it should be part of pervasive computing in future networks. Moreover, it was found that existing studies do not consider the complexities associated with heterogeneous MANET to a large extent leading to narrowed research scope. Hence, this paper introduces a novel scheme called as Secure, Scalable and Interoperable (SSI )routing, where a joint algorithm is designed, developed, and implemented. The outcome exhibits the correctness of this scheme by simulation assisted by analysis for inter-domain routing.
The document describes an agent-based prototype for freight train traffic management between Milano and La Spezia stations in Italy. The prototype was developed using CaseLP, an environment for developing multi-agent system prototypes based on logic programming. The prototype models the complex interactions between various entities involved in freight train scheduling and aims to provide decision support for traffic coordinators. Key aspects of the prototype include defining agent classes and behaviors to represent different system actors like terminals and coordinators, and using agent communication to model coordination between agents. The prototype was tested to evaluate the benefits of an agent-based approach for decision support in freight train traffic management.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Dependency analysis is a technique to detect dependencies between tasks that prevent these tasks from running in parallel. It is an important aspect of parallel programming tools. Dependency analysis techniques are used to determine how much of the code is parallelizable. Literature shows that number of data dependence test has been proposed for parallelizing loops in case of arrays with linear subscripts, however less work has been done for arrays with nonlinear subscripts. GCD test, Banerjee method, Omega test, I-test dependence decision algorithms are used for one-dimensional arrays under constant or variable bounds. However, these approaches perform well only for nested loop with linear array subscripts. The Quadratic programming (QP) test, polynomial variable interval (PVI) test, Range test are typical techniques for nonlinear subscripts. The paper presents survey of these different data dependence analysis tests.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy LogicTELKOMNIKA JOURNAL
In mobile ad hoc networks, route selection is one of the most important issues that is studied in
these networks as a field of research. Many articles trying to provide solutions to choose the best path in
which the important parameters such as power consumption, bandwidth and mobility are used. In this
article, in order to improve the solutions presented in recent papers parameters such as power remaining,
mobility, degree node and available bandwidth are used by taking the factors for each parameter in
proportion to its influence in choosing the best path. Finally, we compare the proposed solution with the
three protocols IAOMDV-F, AODVFART and FLM-AODV with the help of OPNET simulation program
based on network throughput, routing discovery time, the average number of hops per route, network
delay.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
AOTO: Adaptive overlay topology optimization in unstructured P2P systemsZhenyun Zhuang
IEEE GLOBECOM 2003
Peer-to-Peer (P2P) systems are self-organized and
decentralized. However, the mechanism of a peer randomly
joining and leaving a P2P network causes topology mismatch-
ing between the P2P logical overlay network and the physical
underlying network. The topology mismatching problem brings
great stress on the Internet infrastructure and seriously limits
the performance gain from various search or routing tech-
niques. We propose the Adaptive Overlay Topology Optimiza-
tion (AOTO) technique, an algorithm of building an overlay
multicast tree among each source node and its direct logical
neighbors so as to alleviate the mismatching problem by choos-
ing closer nodes as logical neighbors, while providing a larger
query coverage range. AOTO is scalable and completely dis-
tributed in the sense that it does not require global knowledge
of the whole overlay network when each node is optimizing the
organization of its logical neighbors. The simulation shows that
AOTO can effectively solve the mismatching problem and re-
duce more than 55% of the traffic generated by the P2P system itself.
UTILIZING XAI TECHNIQUE TO IMPROVE AUTOENCODER BASED MODEL FOR COMPUTER NETWO...IJCNCJournal
Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of transparency of ML and DL based models is a major obstacle to their implementation and criticized due to its black-box nature, even with such tremendous results. Explainable Artificial Intelligence (XAI) is a promising area that can improve the trustworthiness of these models by giving explanations and interpreting its output. If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance. The objective of this paper is to show that how XAI can be used to interpret the results of the DL model, the autoencoder in this case. And, based on the interpretation, we improved its performance for computer network anomaly detection. The kernel SHAP method, which is based on the shapley values, is used as a novel feature selection technique. This method is used to identify only those features that are actually causing the anomalous behaviour of the set of attack/anomaly instances. Later, these feature sets are used to train and validate the autoencoderbut on benign data only. Finally, the built SHAP_Model outperformed the other two models proposed based on the feature selection method. This whole experiment is conducted on the subset of the latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model is 94% and 0.969, respectively.
A MARKOVIAN MODEL FOR INTERNET OF THINGS APPLICATIONIJCNCJournal
Internet of Things (IoT) allows communication among human-to-things, things-to-human, and things-tothings that are incorporated into an information networks allowing automatic information interchange and
the processing of data at real time. In this paper, we conduct a performance analysis of a real application
defined through four traffic classes with the priorities present in smart cities using Continuous Time
Markov Chains(CTMC). Based on a finite capacity queuing system, we propose a new cost-effective
analytical model with a push-out management scheme in favor of the highest priority (emergency) traffic.
Based on the analytical model, several performance measures for different traffic classes have been
studiedextensively including blocking probability; push out probability, delay, channel utilization as well as
overall system performance.
Privacy Preserving Reputation Calculation in P2P Systems with Homomorphic Enc...IJCNCJournal
This document discusses a method for privacy-preserving reputation calculation in peer-to-peer systems using homomorphic encryption. Specifically, it proposes:
1) Extending the EigenTrust reputation system to calculate node reputations in a distributed manner while preserving evaluator privacy. It does this by successively updating encrypted reputation values through calculation to reflect trust values without disclosing the original values.
2) Improving calculation efficiency by offloading parts of the task to participating nodes and using different public keys during calculation to improve robustness against node churn.
3) Evaluating the performance of the proposed method, finding it reduces maximum circulation time for aggregating multiplication results by half, reducing computation time per round. The privacy preservation cost scales
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Test sequences for web service composition using cpn modelAlexander Decker
The document discusses using a Colored Petri Net (CPN) model to generate test sequences for validating web service compositions. It proposes an algorithm that generates a test suite covering all possible paths without redundancy. The CPN model, previously used to verify the composition design, is reused for test design. The technique was applied to an airline reservation system and test sequences were evaluated against three coverage criteria.
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
Stochastic load balancing for virtual resource management in datacentersFinalyearprojects Toall
To get IEEE 2015-2017 Project for above title in .Net or Java
mail to finalyearprojects2all@gmail.com or contact +91 8870791415
IEEE 2015-2016 Project Videos: https://www.youtube.com/channel/UCyK6peTIU3wPIJxXD0MbNvA
SECURING BGP BY HANDLING DYNAMIC NETWORK BEHAVIOR AND UNBALANCED DATASETSIJCNCJournal
The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.
IMPERATIVE PROGRAMS BEHAVIOR SIMULATION IN TERMS OF COMPOSITIONAL PETRI NETSIJCNCJournal
The document describes a mechanism for generating compositional Petri net models that simulate the behavior of imperative programs. The mechanism consists of two main stages: 1) preparing the program structure using elements like libraries and functions, and 2) generating the contents of function bodies based on semantic construction templates. Some example templates are provided for imperative language constructs like branching, loops, and switch statements. The templates have access points that allow them to be merged via a composition operation to build up function bodies. An example generation is shown where templates for loading, function calls, branching, and termination are combined to model a simple program. The compositional approach allows modeling larger programs by reusing and linking together structural elements and function bodies.
6RLR-ABC: 6LOWPAN ROUTING PROTOCOL WITH LOCAL REPAIR USING BIO INSPIRED ARTIF...IJCNCJournal
The document presents a new routing protocol called 6RLR-ABC for 6LoWPAN networks that uses an artificial bee colony algorithm for local route repair. It compares the performance of 6RLR-ABC to the existing 6LoWPAN Ad-hoc On-Demand Distance Vector (LOAD) protocol through simulations. The results show that 6RLR-ABC achieves lower packet delay, higher packet delivery ratio, and higher throughput while using less energy than LOAD, especially with increasing network traffic loads.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
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.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
DEVELOPMENT AND TESTING OF AN ENERGY HARVESTING TILEIJCI JOURNAL
This paper presents development and experimental analysis of a piezoelectric mounted flexible beam
attached with an oscillating tile that can be used to scavenge energy from footsteps in crowded places. The
energy harvesting system consists of a piezoelectric bimorph cantilever beam inside a hollow box which
connects to the ground with the help of springs. Multiple piezoelectric patches are pasted on the beam.
When the foot is placed on the tile and removed, the hollow box displaces in turn causing the beam to
oscillate harvesting electrical power. A prototype is developed and the performance of the piezoelectric
energy scavenging tile is tested. The variations of power output for different load resistances are obtained
and the optimal load resistance is suggested.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
Improved Routing Protocol in Mobile Ad Hoc Networks Using Fuzzy LogicTELKOMNIKA JOURNAL
In mobile ad hoc networks, route selection is one of the most important issues that is studied in
these networks as a field of research. Many articles trying to provide solutions to choose the best path in
which the important parameters such as power consumption, bandwidth and mobility are used. In this
article, in order to improve the solutions presented in recent papers parameters such as power remaining,
mobility, degree node and available bandwidth are used by taking the factors for each parameter in
proportion to its influence in choosing the best path. Finally, we compare the proposed solution with the
three protocols IAOMDV-F, AODVFART and FLM-AODV with the help of OPNET simulation program
based on network throughput, routing discovery time, the average number of hops per route, network
delay.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
AOTO: Adaptive overlay topology optimization in unstructured P2P systemsZhenyun Zhuang
IEEE GLOBECOM 2003
Peer-to-Peer (P2P) systems are self-organized and
decentralized. However, the mechanism of a peer randomly
joining and leaving a P2P network causes topology mismatch-
ing between the P2P logical overlay network and the physical
underlying network. The topology mismatching problem brings
great stress on the Internet infrastructure and seriously limits
the performance gain from various search or routing tech-
niques. We propose the Adaptive Overlay Topology Optimiza-
tion (AOTO) technique, an algorithm of building an overlay
multicast tree among each source node and its direct logical
neighbors so as to alleviate the mismatching problem by choos-
ing closer nodes as logical neighbors, while providing a larger
query coverage range. AOTO is scalable and completely dis-
tributed in the sense that it does not require global knowledge
of the whole overlay network when each node is optimizing the
organization of its logical neighbors. The simulation shows that
AOTO can effectively solve the mismatching problem and re-
duce more than 55% of the traffic generated by the P2P system itself.
UTILIZING XAI TECHNIQUE TO IMPROVE AUTOENCODER BASED MODEL FOR COMPUTER NETWO...IJCNCJournal
Machine learning (ML) and Deep Learning (DL) methods are being adopted rapidly, especially in computer network security, such as fraud detection, network anomaly detection, intrusion detection, and much more. However, the lack of transparency of ML and DL based models is a major obstacle to their implementation and criticized due to its black-box nature, even with such tremendous results. Explainable Artificial Intelligence (XAI) is a promising area that can improve the trustworthiness of these models by giving explanations and interpreting its output. If the internal working of the ML and DL based models is understandable, then it can further help to improve its performance. The objective of this paper is to show that how XAI can be used to interpret the results of the DL model, the autoencoder in this case. And, based on the interpretation, we improved its performance for computer network anomaly detection. The kernel SHAP method, which is based on the shapley values, is used as a novel feature selection technique. This method is used to identify only those features that are actually causing the anomalous behaviour of the set of attack/anomaly instances. Later, these feature sets are used to train and validate the autoencoderbut on benign data only. Finally, the built SHAP_Model outperformed the other two models proposed based on the feature selection method. This whole experiment is conducted on the subset of the latest CICIDS2017 network dataset. The overall accuracy and AUC of SHAP_Model is 94% and 0.969, respectively.
A MARKOVIAN MODEL FOR INTERNET OF THINGS APPLICATIONIJCNCJournal
Internet of Things (IoT) allows communication among human-to-things, things-to-human, and things-tothings that are incorporated into an information networks allowing automatic information interchange and
the processing of data at real time. In this paper, we conduct a performance analysis of a real application
defined through four traffic classes with the priorities present in smart cities using Continuous Time
Markov Chains(CTMC). Based on a finite capacity queuing system, we propose a new cost-effective
analytical model with a push-out management scheme in favor of the highest priority (emergency) traffic.
Based on the analytical model, several performance measures for different traffic classes have been
studiedextensively including blocking probability; push out probability, delay, channel utilization as well as
overall system performance.
Privacy Preserving Reputation Calculation in P2P Systems with Homomorphic Enc...IJCNCJournal
This document discusses a method for privacy-preserving reputation calculation in peer-to-peer systems using homomorphic encryption. Specifically, it proposes:
1) Extending the EigenTrust reputation system to calculate node reputations in a distributed manner while preserving evaluator privacy. It does this by successively updating encrypted reputation values through calculation to reflect trust values without disclosing the original values.
2) Improving calculation efficiency by offloading parts of the task to participating nodes and using different public keys during calculation to improve robustness against node churn.
3) Evaluating the performance of the proposed method, finding it reduces maximum circulation time for aggregating multiplication results by half, reducing computation time per round. The privacy preservation cost scales
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Test sequences for web service composition using cpn modelAlexander Decker
The document discusses using a Colored Petri Net (CPN) model to generate test sequences for validating web service compositions. It proposes an algorithm that generates a test suite covering all possible paths without redundancy. The CPN model, previously used to verify the composition design, is reused for test design. The technique was applied to an airline reservation system and test sequences were evaluated against three coverage criteria.
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
Stochastic load balancing for virtual resource management in datacentersFinalyearprojects Toall
To get IEEE 2015-2017 Project for above title in .Net or Java
mail to finalyearprojects2all@gmail.com or contact +91 8870791415
IEEE 2015-2016 Project Videos: https://www.youtube.com/channel/UCyK6peTIU3wPIJxXD0MbNvA
SECURING BGP BY HANDLING DYNAMIC NETWORK BEHAVIOR AND UNBALANCED DATASETSIJCNCJournal
The Border Gateway Protocol (BGP) provides crucial routing information for the Internet infrastructure. A problem with abnormal routing behavior affects the stability and connectivity of the global Internet. The biggest hurdles in detecting BGP attacks are extremely unbalanced data set category distribution and the dynamic nature of the network. This unbalanced class distribution and dynamic nature of the network results in the classifier's inferior performance. In this paper we proposed an efficient approach to properly managing these problems, the proposed approach tackles the unbalanced classification of datasets by turning the problem of binary classification into a problem of multiclass classification. This is achieved by splitting the majority-class samples evenly into multiple segments using Affinity Propagation, where the number of segments is chosen so that the number of samples in any segment closely matches the minority-class samples. Such sections of the dataset together with the minor class are then viewed as different classes and used to train the Extreme Learning Machine (ELM). The RIPE and BCNET datasets are used to evaluate the performance of the proposed technique. When no feature selection is used, the proposed technique improves the F1 score by 1.9% compared to state-of-the-art techniques. With the Fischer feature selection algorithm, the proposed algorithm achieved the highest F1 score of 76.3%, which was a 1.7% improvement over the compared ones. Additionally, the MIQ feature selection technique improves the accuracy by 3.5%. For the BCNET dataset, the proposed technique improves the F1 score by 1.8% for the Fisher feature selection technique. The experimental findings support the substantial improvement in performance from previous approaches by the new technique.
IMPERATIVE PROGRAMS BEHAVIOR SIMULATION IN TERMS OF COMPOSITIONAL PETRI NETSIJCNCJournal
The document describes a mechanism for generating compositional Petri net models that simulate the behavior of imperative programs. The mechanism consists of two main stages: 1) preparing the program structure using elements like libraries and functions, and 2) generating the contents of function bodies based on semantic construction templates. Some example templates are provided for imperative language constructs like branching, loops, and switch statements. The templates have access points that allow them to be merged via a composition operation to build up function bodies. An example generation is shown where templates for loading, function calls, branching, and termination are combined to model a simple program. The compositional approach allows modeling larger programs by reusing and linking together structural elements and function bodies.
6RLR-ABC: 6LOWPAN ROUTING PROTOCOL WITH LOCAL REPAIR USING BIO INSPIRED ARTIF...IJCNCJournal
The document presents a new routing protocol called 6RLR-ABC for 6LoWPAN networks that uses an artificial bee colony algorithm for local route repair. It compares the performance of 6RLR-ABC to the existing 6LoWPAN Ad-hoc On-Demand Distance Vector (LOAD) protocol through simulations. The results show that 6RLR-ABC achieves lower packet delay, higher packet delivery ratio, and higher throughput while using less energy than LOAD, especially with increasing network traffic loads.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
This paper is written for predicting Bankruptcy using different Machine Learning Algorithms. Whether the company will go bankrupt or not is one of the most challenging and toughest question to answer in the 21st Century. Bankruptcy is defined as the final stage of failure for a firm. A company declares that it has gone bankrupt when at that present moment it does not have enough funds to pay the creditors. It is a global
problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
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.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
DEVELOPMENT AND TESTING OF AN ENERGY HARVESTING TILEIJCI JOURNAL
This paper presents development and experimental analysis of a piezoelectric mounted flexible beam
attached with an oscillating tile that can be used to scavenge energy from footsteps in crowded places. The
energy harvesting system consists of a piezoelectric bimorph cantilever beam inside a hollow box which
connects to the ground with the help of springs. Multiple piezoelectric patches are pasted on the beam.
When the foot is placed on the tile and removed, the hollow box displaces in turn causing the beam to
oscillate harvesting electrical power. A prototype is developed and the performance of the piezoelectric
energy scavenging tile is tested. The variations of power output for different load resistances are obtained
and the optimal load resistance is suggested.
COMPARATIVE STUDY ON VEHICLE DETECTION TECHNIQUES IN AERIAL SURVEILLANCEIJCI JOURNAL
Aerial surveillance system becomes a great trendy on past decades. Aerial surveillance vehicle tracking techniques plays a vital role and give rising to optimistic techniques continuously. This system can be very handy in various applications such as police, traffic monitoring, natural disaster and military. It is often covers large area and providing better perspective of moving objects. The detection of moving vehicle can be both from the dynamic aerial imagery, wide area motion imagery or images under low resolution and also the static in nature. It has been very difficult issue whether identify the object in the air view, the camera angles, movement objects and motionless object. This paper deals with comparative study on various vehicle detection and tracking approach in aerial videos with its experimental results and measures working condition, hit rate and false alarm rate
Adopting level set theory based algorithms to segment human earIJCI JOURNAL
Human identification has always been a topic that interested researchers around the world. Biometric methods are found to be more effective and much easier for the users than the traditional identification methods like keys, smart cards and passwords. Unlike with the traditional methods, with biometric methods the data acquisition is most of the times passive, which means the users do not take active part in data acquisition. Data acquisition can be performed using cameras, scanners or sensors. Human physiological biometrics such as face, eye and ear are good candidates for uniquely identifying an individual. However, human ear scores over face and eye because of certain advantages it has over face. The most challenging phase in human identification based on ear biometric is the segmentation of the ear image from the captured image which may contain many unwanted details. In this work, PDE based image processing techniques are used to segment out the ear image. Level Set Theory based image processing is employed to obtain the contour of the ear image. A few Level set algorithms are compared for their efficiency in segmenting test ear images
Integral imaging three dimensional (3 d)IJCI JOURNAL
This document proposes a three-dimensional integral imaging display system that uses multiple illuminations to improve the viewing angle. The proposed system uses three collimated light sources directed at different angles to generate point light sources with a wider propagation angle compared to a conventional single illumination system. Simulation results show that the viewing angle of the proposed system is three times larger than conventional point light source displays. The proposed multiple illumination system allows continuous viewing of reconstructed 3D objects over a wider angle without loss of intensity or quality.
This document discusses optimal control problems for stochastic sequential machines (SSMs). It begins by introducing SSMs and defining their components. It then formulates the optimal control problem for processes represented by SSMs, proving the principle of optimality. Using dynamic programming, it derives the Bellman equation to find the optimal control solution. In conclusions, it shows that the Bellman equation and principle of optimality apply to obtaining the optimal control for processes modeled as SSMs.
An intrusion detection algorithm for amiIJCI JOURNAL
Nowadays, using the smart metering devices for energy users to manage a wide variety of subscribers,
reading devices for measuring, billing, disconnection and connection of subscribers’ connection
management is an important issue. The performance of these intelligent systems is based on information
transfer in the context of information technology, so reported data from network should be managed to
avoid the malicious activities that including the issues that could affect the quality of service the system. In
this paper for control of the reported data and to ensure the veracity of the obtained information, using
intrusion detection system is proposed based on the support vector machine and principle component
analysis (PCA) to recognize and identify the intrusions and attacks in the smart grid. Here, the operation of
intrusion detection systems for different kernel of SVM when using support vector machine (SVM) and PCA
simultaneously is studied. To evaluate the algorithm, based on data KDD99, numerical simulation is done
on five different kernels for an intrusion detection system using support vector machine with PCA
simultaneously. Also comparison analysis is investigated for presented intrusion detection algorithm in
terms of time - response, rate of increase network efficiency and increase system error and differences in
the use or lack of use PCA. The results indicate that correct detection rate and the rate of attack error
detection have best value when PCA is used, and when the core of algorithm is radial type, in SVM
algorithm reduces the time for data analysis and enhances performance of intrusion detection.
Generalized synchronization of nonlinear oscillators via opcl couplingIJCI JOURNAL
In this communication, open-plus-closed-loop (OPCL) coupling method is applied to make generalized
synchronization between two non-linear chaotic dynamical systems. For this reason, a transformation
matrix is considered which can be chosen arbitrarily. We have used five different cases to establish our
claim. Chaotic behaviours and the efficiency of the generalized synchronization using OPCL method are
verified by numerical simulations.
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
Possibility fuzzy c means clustering for expression invariant face recognitionIJCI JOURNAL
Face being the most natural method of identification for humans is one of the most significant biometric
modalities and various methods to achieve efficient face recognition have been proposed. However the
changes in face owing to different expressions, pose, makeup, illumination, age bring about marked
variations in the facial image. These changes will inevitably occur and they can be controlled only till a
certain degree beyond which they are bound to happen and will affect the face thereby adversely impacting
the performance of any face recognition system. This paper proposes a strategy to improve the
classification methodology in face recognition by using Possibility Fuzzy C-Means Clustering (PFCM).
This clustering technique was used for face recognition due to its properties like outlier insensitivity which
make it a suitable candidate for use in designing such robust applications.PFCM is a hybridization of
Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) clustering algorithms. PFCM is a robust
clustering technique and is especially significant for its noise insensitivity. It has also resolved the
coincident clusters problem which is faced by other clustering techniques. Therefore the technique can also
be used to increase the overall robustness of a face recognition system and thereby increase its invariance
and make it a reliably usable biometric modality.
Report: A Model For Remote Parental Control System Using SmartphonesIJCI JOURNAL
The document summarizes a research paper that proposes a model called "RePort" for remotely monitoring children and teens' access and online activities via their computers and smartphones. The 4-layer RePort model includes layers to monitor smartphone usage through call logs, SMS, GPS, WiFi, and Bluetooth; desktop computer usage; social networking activities; and generate reports. It was implemented as an Android app and Windows service to track parameters like time spent, posts, and messages. Experiments validated the model's ability to monitor access behaviors remotely with 93.46% accuracy.
Enhanced Thinning Based Finger Print RecognitionIJCI JOURNAL
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
Technical and Financial Analysis of Photovoltaic Water Pumping System for GOR...IJCI JOURNAL
Because of human need for energy, extra special attention is in the usage of renewable-energy sources in
recent years. On the other hand, environmental pollution is created with fossil energy. Photovoltaic (PV)
energy is also one of the renewable-energy sources that are available in almost all parts of the globe,
especially in Iran. One application of this energy is in water pumping system. In this paper, we precede the
technical and financial study on photovoltaic water pumping system for irrigation of GORGAN’s farm
fields (one of Northern Province of Iran) with the RETScreen software tools. In order to the results, it is
obvious that the usage of this clean energy causes the reduction on production costs during of its operation.
Simple practice based on the platform designIJCI JOURNAL
The cloud virtualization and N-Screen technologies related to open cloud environment are promoting to
change legacy IT service architecture. The technologies are very useful to transform old service
architecture to new one. With the technologies, Smart TV and its service architecture can be improved.
Smart TV has been discussed as a promising device of Post PC category to handle various user needs by
adding computing power to general TV. Smart TV is already commercialized and used in web-surfing, ondemand
requests on movies combined with Internet enabled set-top box device. There has been specific
approach to increase its usability by adding TV apps for specific Smart TV hardware. However, as Post PC
perspective, current Smart TV system and architecture are lack of flexibility and need new paradigm. The
architecture should provide office-work friendly environment, cover various OS-dependent users and apps
based on Android OS & iOS together, and support legacy IT resources. In this paper, we suggest new
platform design to achieve the goal to make Smart TV as Post PC device based on emerging cloud
virtualization & N-Screen technologies and develop a simple set to test main functions of the platform.
A PPLICATION OF C LASSICAL E NCRYPTION T ECHNIQUES FOR S ECURING D ATA -...IJCI JOURNAL
The process of protecting information by transformi
ng (encrypting) it into an unreadable format is cal
led
cryptography. Only those who possess secret key can
decipher (decrypt) the message into plain text.
Encrypted messages can sometimes be broken by crypt
analysis, also called code breaking, so there is a
need for strong and fast cryptographic methods for
securing the data from attackers. Although modern
cryptography techniques are virtually unbreakable,
sometimes they also tend to attack.
As the Internet, big data, cloud data storage and
other forms of electronic communication become more
prevalent, electronic security is becoming increasi
ngly important. Cryptography is used to protect e-m
ail
messages, credit card information, corporate data,
cloud data and big data so on... So there is a need
for
best and fast cryptographic methods for protecting
the data. In this paper a method is proposed to pro
tect
the data in faster way by using classical cryptogra
phy. The encryption and decryption are done in par
allel
using threads with the help of underlying hardware.
The time taken by sequential and parallel method i
s
analysed
F EATURE S ELECTION USING F ISHER ’ S R ATIO T ECHNIQUE FOR A UTOMATIC ...IJCI JOURNAL
Automatic Speech Recognition (ASR) involves mainly
two steps; feature extraction and classification
(pattern recognition). Mel Frequency Cepstral Coeff
icient (MFCC) is used as one of the prominent featu
re
extraction techniques in ASR. Usually, the set of a
ll 12 MFCC coefficients is used as the feature vect
or in
the classification step. But the question is whethe
r the same or improved classification accuracy can
be
achieved by using a subset of 12 MFCC as feature ve
ctor. In this paper, Fisher’s ratio technique is us
ed for
selecting a subset of 12 MFCC coefficients that con
tribute more in discriminating a pattern. The selec
ted
coefficients are used in classification with Hidden
Markov Model (HMM) algorithm. The classification
accuracies that we get by using 12 coefficients and
by using the selected coefficients are compare
NEAR FIELD COMMUNICATION (NFC) TECHNOLOGY: A SURVEY IJCI JOURNAL
Near Field Communication, NFC- is one of the latest
short range wireless communication technologies.
NFC provides safe communication between electronic
gadgets. NFC-enabled devices can just be pointed or
touched by the users of their devices to other NFC-
enabled devices to communicate with them. With NFC
technology, communication is established when an NF
C-compatible device is brought within a few
centimetres of another i.e. around 20 cm theoretica
lly (4cm is practical). The immense benefit of the
short
transmission range is that it prevents eavesdroppin
g on NFC-enabled dealings. NFC technology enables
several innovative usage scenarios for mobile devic
es. NFC technology works on the basis of RFID
technology which uses magnetic field induction to c
ommence communication between electronic devices in
close vicinity. NFC operates at 13.56MHz and has 42
4kbps maximum data transfer rate. NFC is
complementary to Bluetooth and 802.11 with their lo
ng distance capabilities. In card emulation mode NF
C
devices can offer contactless/wireless smart card s
tandard. This technology enables smart phones to
replace traditional plastic cards for the purpose o
f ticketing, payment, etc. Sharing (share files bet
ween
phones), service discovery i.e. get information by
touching smart phones etc. are other possible
applications of NFC using smart phones. This paper
provides an overview of NFC technology in a detaile
d
manner including working principle, transmission de
tails, protocols and standards, application scenari
os,
future market, security standards and vendor’s chip
sets which are available for this standard. This
comprehensive survey should serve as a useful guide
for students, researchers and academicians who are
interested in NFC Technology and its applications [
1].
Integrating Vague Association Mining with Markov Model ijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.
An Efficient Hybrid Successive Markov Model for Predicting Web User Usage Beh...Waqas Tariq
With the continued growth and proliferation of Web services and Web based information systems, the volumes of user data have reached astronomical proportions. Analyzing such data using Web Usage Mining can help to determine the visiting interests or needs of the web user. As web log is incremental in nature, it becomes a crucial issue to predict exactly the ways how users browse websites. It is necessary for web miners to use predictive mining techniques to filter the unwanted categories for reducing the operational scope. The first-order Markov model has low accuracy in achieving right predictions, which is why extensions to higher order models are necessary. All higher order Markov model holds the promise of achieving higher prediction accuracies, improved coverage than any single-order Markov model but holds high state space complexity. Hence a Hybrid Markov Model is required to improve the operation performance and prediction accuracy significantly. The present paper introduces An Efficient Hybrid Successive Markov Prediction Model, HSMP. The HSMP model is initially predicts the possible wanted categories using Relevance factor, which can be used to infer the users’ browsing behavior between web categories. Then predict the pages in predicted categories using techniques for intelligently combining different order Markov models so that the resulting model has low state complexity, improved prediction accuracy and retains the coverage of the all higher order Markov model. These techniques eliminates low support states, evaluates the probability distribution and estimates the error associated with each state without affecting the overall accuracy as well as protection of the resulting model. To validate the proposed prediction model, several experiments were conducted and results proven this are claimed in this paper.
Improvement of a method based on hidden markov model for clustering web userscsandit
Nowadays the determination of the dynamics of sequential data, such as marketing, finance,
social sciences or web research has receives much attention from researchers and scholars.
Clustering of such data by nature is always a more challenging task. This paper investigates the
applications of different Markov models in web mining and improves a developed method for
clustering web users, using hidden Markov models. In the first step, the categorical sequences
are transformed into a probabilistic space by hidden Markov model. Then, in the second step,
hierarchical clustering, the performance of clustering process is evaluated with various
distances criteria. Furthermore this paper shows implementation of the proposed improvements
with symmetric distance measure as Total-Variance and Mahalanobis compared with the
previous use of the proposed method (such as Kullback–Leibler) on the well-known Microsoft
dataset with website user search patterns is more clearly result in separate clusters.
A vague improved markov model approach for web page predictionIJCSES Journal
Today most of the information in all areas is available over the web. It increases the web utilization as
well as attracts the interest of researchers to improve the effectiveness of web access and web utilization.
As the number of web clients gets increased, the bandwidth sharing is performed that decreases the web
access efficiency. Web page prefetching improves the effectiveness of web access by availing the next
required web page before the user demand. It is an intelligent predictive mining that analyze the user web
access history and predict the next page. In this work, vague improved markov model is presented to
perform the prediction. In this work, vague rules are suggested to perform the pruning at different levels of
markov model. Once the prediction table is generated, the association mining will be implemented to
identify the most effective next page. In this paper, an integrated model is suggested to improve the
prediction accuracy and effectiveness.
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
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.
An Adjacent Analysis of the Parallel Programming Model Perspective: A SurveyIRJET Journal
This document provides an overview and analysis of parallel programming models. It begins with an abstract discussing the growing demand for parallel computing and challenges with existing parallel programming frameworks. It then reviews several relevant studies on parallel programming models and architectures. The document goes on to describe several key parallel programming models in more detail, including the Parallel Random Access Machine (PRAM) model, Unrestricted Message Passing (UMP) model, and Bulk Synchronous Parallel (BSP) model. It discusses aspects of each model like architecture, communication methods, and associated cost models. The overall goal is to compare benefits and limitations of different parallel programming models.
Prediction Model Using Web Usage Mining TechniquesEditor IJCATR
Popularity of WWW increasing day by day which results in the increase of web based services , due to which web is now
largest data repository. In order to handle this incremental nature of data various prediction techniques are used. If the prefetched pages
are not visited by user in their subsequent access there will be wastage network bandwidth as it is in limited amount. So there is critical
requirement of accurate prediction method. As the data present on web is heterogeneous in nature and incremental in nature, during the
pre-processing step hierarchical clustering technique is used. Then using Markov model category and page prediction is done and
lastly page filtering is done using keywords.
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.
Testing is important for Web applications as they grow more complex day by day which affects daily life.
Web application testing frameworks have emerged to help satisfy this need. In this a model is developed for
the web applications and test cases are generated by combining operational profile and random testing
technique. Those test cases are applied to test the actual website. This research work describes a practical
oriented approach to engineer automated tests for web applications with reference to a web application
development.
A comprehensive review on hybrid network traffic prediction model IJECEIAES
Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.
Semantic web based software engineering by automated requirements ontology ge...IJwest
This paper presents an approach for automated generation of requirements ontology using UML diagrams in service-oriented architecture (SOA). The goal of this paper is to convenience progress of software engineering processes like software design, software reuse, service discovering and etc. The proposed method is based on a four conceptual layers. The first layer includes requirements achieved by stakeholders, the second one designs service-oriented diagrams from the data in first layer and extracts XMI codes of them. The third layer includes requirement ontology and protocol ontology to describe behavior of services and relationships between them semantically. Finally the forth layer makes standard the concepts exists in ontologies of previous layer. The generated ontology exceeds absolute domain ontology because it considers the behavior of services moreover the hierarchical relationship of them. Experimental results conducted on a set of UML4Soa diagrams in different scopes demonstrate the improvement of the proposed approach from different points of view such as: completeness of requirements ontology, automatic generation and considering SOA.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
Recently with the increasing development of distributed computer systems (DCSs) in networked
industrial and manufacturing applications on the World Wide Web (WWW) platform, including service-oriented
architecture and Web of Things QoS-aware systems, it has become important to predict the Web performance.
In this paper, we present Web performance prediction in time by making a forecast of a Web resource
downloading using the Efficient Turning Bands (TB) geostatistical simulation method. Real-life data for the
research were obtained from our own website named "Distributed forecasting system". Generation of log file
form website and performing monitoring of a group of Web clients from connected LAN. For better web
prediction we used spatio temporal prediction method with time utility for downloading particular file from
website and calculate forecasting result using Turning bands method but improving more forecasting
accuracy use the efficient turning band method basically efficient turning band use Naive bays algorithm and
calculate efficient result and that result is compared with Turning band and efficient turning band method.
The efficient turning band method result show good forecasting quality of Web performance prediction and
forecasting.
Performance analysis of routing protocol in MANETjannahyusoff1
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Evaluation of models for predicting user’s next request in web usage mining
1. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 201
DOI: 10.5121/ijci.2015.4101 1
Evaluation of Models for Predicting User’s Next
Request in Web Usage Mining
Bhawna Nigam
Department of Information Technology ,Institute of Engineering & Technology,
DAVV Indore, M.P., India
Dr. Sanjiv Tokekar
Department of Electronics & Telecommunication Engineering Institute of Engineering &
Technology, DAVV Indore, M.P., India
Dr. Suresh Jain
Department of Computer Engg. Prestige Institute of Engineering Management &
Research, Indore, INDIA
Abstract
Prediction of web user behavior is the demand of today competitive edge of World Wide Web. Predicting the
next web page is not sufficient, evaluation of prediction models is important because every model have its
own pros and cons. Prediction results will be helpful if high prediction accuracy is achieved with minimum
complexity, which are depended on the prediction model. Various models and their variations are proposed
for predicting the next web page accessed by the web user. Markov model and their variations are found
suitable for web prediction. In this research we have evaluated and compared various models for predicting
next web page accessed by the web user. Experiments are conducted on three different real datasets.
Keywords
Web usage mining, Markov Model, User Navigation Session, web log and web prediction
1.Introduction
Web prediction is a field of web usage mining in which next accessed web page by web user is
predicted. Prediction results can be used for personalization of web, reducing the server
response time with proper prefetching and caching strategies [1]. It can provide guidelines for
improving the design of web applications, e-commerce to handle business specific issues like
customer attraction, customer retention, cross sales and customer departure.
Prediction of next access web page can be achieved through modelling the web log with the help
of model. The logging information is stored in a file known as web log file which resides on web
server, proxy server or client cite. The web log file is the text file which contains lots of
information such as IP address, date, time, request type etc., so it is preprocessed before
modelling. From the preprocessed web log information the user navigation session prepared. The
user navigation session is finally modeled through a model. Once the user navigation model is
ready, the mining task can be performed to predict next accessed web page. In prediction model
log file is divided into two parts training file and testing file, training file is used to build the
model and testing file is used to test the model. Various models have been proposed to
accomplish this task such as Markov Model, Semantic Model, Dynamic Nested Markov Model,
association rule mining and many more[3,4].
2. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
2
Predicting the next web page is not sufficient, it is also very important to evaluate the prediction
because every model has their advantages and limitations [9]. Lower order Markov model is less
complex with low accuracy and high order Markov model has high accuracy with high
complexity and low coverage. So before uploading any prediction model on the server, it is very
necessary to find limitations of the model. Various parameters are there to evaluate the prediction
such as how much time required by model to predict next web page, prediction accuracy,
generation time, coverage and many more. In this work we have evaluated and compared
different models for mining the web log to predict next accessed web page.
2.Related Work
Several authors have proposed models for modelling the user navigation session to predict next
accessed web page. Markov model is widely used for modelling the web navigation sessions.
Markov model is based on a well established theory. F. Khalil et al. [5], have proposed a new
framework for predicting the next web page access. In this they have study the Markov model for
prediction. If the Markov model is not able to predict the next web page then the association rule
are used for predicting the next web page. They have also proposed that if there will be ambiguity
in the prediction, it will be resolve by association rule. J. Borges et al. [6, 7, 8], have proposed the
Higher-order Markov model with clustering technique to improve the effectiveness of Markov
Model. The K-mean clustering technique has been used to reduce the state space complexity. F.
Khalil et al. [10] have proposed the integrated approach for predicting the next access web page
where they have tried to achieve the high level of predictive accuracy with low state space
complexity. Siriporn Chimphlee et al. [11], used association rule for next access prediction. Nizar
R. [12] proposed semantic rich markov model for web prefetching. B. Nigam et al. [13] used the
concept of dynamic nested markov model to predict next accessed web page whose analysis is
done on different schemes of prefetching and caching [14]. M.T. Hassan et al. [17] presented
Bayesian Models for two things like learning and predicting key Web navigation patterns. Instead
of modelling the general problem of Web navigation they focus on key navigation patterns that
have practical value. Mamoun A. Awad et al. [18], analyzed and studied Markov Model with all-
Kth Markov Model for web prediction. They proposed a new modified Markov Model to
alleviate the issue of scalability in the number of paths. Poornalatha G et al. [19] presented a
paper to solve the problem of predicting the next web page to be accessed by the user based on
the mining of web server logs that maintains the information of users who access the web site.
Section 2, describes the Markov Model and Dynamic Nested Markov Model, Section 3, describes
the experimental results and finally section 4 describes the future work and conclusion.
3.Prediction Models
Markov Model is compact, simple, expressive and based on a well-established theory. Markov
Model is widely used to model user navigation sessions. In first-order Markov Model, each state
corresponds to a web page and each pair of viewed web page corresponds to state transition. Two
artificial state i.e. start and final, are incorporated in the model. In second-order Markov Model,
each state corresponds to sequence of two viewed web pages and so on.
3. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
3
A) Generation Second-Order Markov Model
Markov Model is widely used to model user navigation sessions. Two artificial states i.e. start and
final are incorporated in the model. In first-order Markov Model, each state corresponds to a web
page and each pair of viewed page corresponds to state transition. In second-order Markov
Model, each state corresponds to sequence of two viewed web pages and so on. Figure 5.2 shows
the corresponding transition diagram of Hypertext Probabilistic Grammar. Hypertext Probabilistic
Grammar (HPG) is a four-tuple <V, Σ, S, P>, V={A1, A2, A3….} is set of non-terminals , Σ ={a1,
a2, a3….} is set of terminal symbol, S is start symbol , P is set of production rule.
if the probability of state being in a start production is proportional to the total number of
times the state was visited in the collection of navigation sessions. Therefore, when the
probability of a start production is proportional to the number of times the corresponding state
was visited, implying that the destination node of a production with higher probability
corresponds to a state that was visited more often. The parameter can take any value between 0
and 1, providing a balance between the two scenarios described above. As such, gives the
analyst the ability to tune the model for the search of different types of patterns in the user
navigation. Finally, the probability of a transitive production is assigned in such a way that it is
proportional to the frequency with which the corresponding link was traversed.
Table 1 shows the example of collection of training and testing user navigation sessions. T1 to T5
are the transaction ID. There are five web pages P1 to P5.
Table 1: Collection of User Navigation Sessions.
Transaction ID Training Sessions
T1 P2, P3, P1, P5
T2 P2, P1, P3, P4, P5
T3 P1, P2, P5
T4 P1, P5, P4
T5 P1, P2, P4
Transaction ID Testing Sessions
T1 P3, P1, P4
T2 P1, P5, P4
T3 P4, P5
4. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
4
Fig. 1: Second-order Markov Model corresponds to training file of table 1.
Fig. 1 shows the second-order Markov Model corresponds to the training file of table 1. The model
is represented with the hypertext weighted Matrix. Here states are the sequence of two viewed web
page, S is the start state and F is final state.
Table 2 shows Hypertext weighted Matrix for second-order Markov Model. if the state exists then
the weight shows the count of number of times the sequence occurs in the training file otherwise
it will be 0.
Table 2: Hypertext Weighted Matrix.
2nd
Order Markov Model P1 P2 P3 P4 P5
{P1, P2} 0 0 0 1 1
{P1, P3} 0 0 0 1 0
{P1, P5} 0 0 0 1 0
{P2, P1} 0 0 1 0 0
{P2, P3} 1 0 0 0 0
{P2, P4} 0 0 0 0 0
{P2, P5} 0 0 0 0 0
{P3, P1} 0 0 0 0 1
{P3, P4} 0 0 0 0 1
{P4, P5} 0 0 0 0 0
{P5, P4} 0 0 0 0 0
5. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
5
Table 3: Prediction results of second-order Markov Model.
Last web page Original web page Predicted web page Correct web page
{P3, P1} P4 P5 X
{P1, P5} P4 P4 √
{P4} P5 Cannot be predicted No Result
Table 3 shows the prediction results of second-order Markov Model. The prediction accuracy is
33 %. Test session P4 cannot be predicted with the help of second-order markov model. The
coverage of the model becomes 50% because it cannot predict the single state.
B) Generation of Dynamic Nested Markov mode
In Dynamic Nested Markov Model the higher-order Markov Model is nested inside the lower-
order Markov Model [13, 14]. DNMM uses the link list structure for storing the information of
web page. DNMM is same as Markov Model with some changes so that the efficiency of model
can be enhanced. This model is dynamic in nature means the addition and deletion of state can be
done easily. This model uses the node structure to store the web page. All the information of a
particular web page is stored in a node of that web page. In this model, only one node per web
page is created. Node is a dynamic data structure rather than just name of the web page. Each
node contains name of web page and an in-link-list. The inlink list is a link list in which each
node contain name of a previous web page from which the current web page is traversed, count
that shows number of times current web page is traversed from previous web page and an outlink
list that keep track of all the corresponding to that previous web page. Outlink list is a linked list
whose each node contains name of next web page and its count. Now this data structure keeps
track of all the previous web pages and all the next web pages corresponding to each previous
web page of the current node. In third order model every node contain data upto third order and in
fourth order each node contain data up to fourth order, but number of nodes are always constant.
In this way, we can model user navigation sessions in highly structured and efficient way.
In DNMM each web page is represented by unique node. All the information regarding a
particular web page is stored inside that node up to the n-order model. Figure 2 shows the node
structure of web page Wx for second-order DNMM. W1, W2…. are the second-order inlinks to
the web page Wx and from Wx the corresponding second-order outlinks are shown in figure 2.
6. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
6
Fig. 2: Node structure of Second-Order Dynamic Nested Markov Model.
Figure 3 shows the first-order DNMM corresponds to the table 1. S and E i.e. start and end is the
two artificial states incorporated in model. First-order DNMM construction starts with traversing
the first user navigation sessions of web log. If the traversed web page does not exists then the
node of that web page is created and corresponding f_inlink count and f_outlink list which has
next web page name and count will be created. Firstly, P2, P3, P1, P5 will be traversed because it is
the first sessions and there is no node existing previously. Node of P2 web page will be created,
f_inlink count set to 1 and f_outlink list which contains P3 as next web page name and count is 1.
Next P3 web page is created, f_inlink count set to 1, and f_outlink list will have P1 as next web
page name and count is set to 1. Now P1 web page will created, f_inlink count is set to 1 and
f_outlink has P5 as next web page and count is 1. This way the first-order DNMM is created. The
first-order Model DNMM is almost like the traditional first-order Markov Model.
Fig. 3: First-Order Dynamic Nested Markov Model corresponds to the training file of table 1.
7. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
7
Figure 4 shows second-order Dynamic Nested Markov Model corresponds to the table 1. In the
second-order DNMM history of previous web page is stored inside the node of first-order model.
The model construction starts with the first user navigation sessions i.e. P2, P3, P1 and P5. As it is
the first user navigation sessions of the training file and no node exists till now, so the P2 node
will be created. inlink of P2 node is created and named as S because P2 is starting web page and
S is considered to be its previous web page. Now the outlink list of inlink S will be updated as P3
web page and its count set to 1. Now P3 is traversed which is also not exists in the model so it is
created. P2 is created as inlink and count set to 1 because P2 is traversed one time from P3. Now
the outlink P1 for inlink P2 is created and its count set to 1. Similarly node P1 will be created.
When P5 node is created, its inlink P1 is created which has E as outlink. This way the first user
navigation sessions has been modelled. Second user navigation sessions is P2, P1, P3, P4, P5. Its
first web page is P2 which is already exist in the model. It has S as its inlink so count will be
incremented by 1 and will become 2. Outlink P1 will be checked in inlink of S which does not
exist, so it is created and count set to 1. Web page P1 is traversed and node P1 is present in model.
Node P1 has an inlink P3. Another inlink P2 is created and its outlink P3 is created. Similarly, web
page P3 is traversed and its node is updated. When web page P4 is traversed its node does not
exist, so it will be created and updated similarly. Remaining user navigation sessions are
modelled in same way.
Fig. 4: Second-order Dynamic Nested Markov Model corresponds to example of table 1.
8. International Journal on Cybernetics & Informatics (IJCI) Vol. 4, No. 1, February 2015
8
Table 4: Prediction results of second-order DNMM.
Last web page Original web page Predicted web page Correct web page
{P3, P1} P4 P5 X
{P1, P5} P4 P4 √
{P4} P5 P5 √
Table 4 shows the Prediction results of second-order DNMM. There are three test sessions out of
which two were predicted right. The prediction accuracy is 66% because it can predict the single
state also. That is why its coverage is 100%.
4. Experiment Result:
Data Sets:
The experimental data set were obtained from three different data sources. First experimental
weblog data is collected from Cuboid Pvt. Ltd., Indore. Second weblog data is MSNBC, collected
from UCI repository and can be downloaded from
https://archive.ics.uci.edu/ml/datasets/MSNBC.com+Anonymous+Web+Data. Third experimental
weblog data were obtained from the authors Jose Borges and Mark Levene and downloaded from
http://www.cs. washington.edu/homes/map/adaptive/download.html. Two months web log data
are obtained from this website for experiments. The web site is http://machines.hyperreal.org. It
is given that web site receives approximately 10000 requests per day from around 1200 users.
Table 5 summarizes the characteristics of the web log data sets. The training data set and testing
data set characteristic are given below.
Table 5: Summary of Web Log Datasets (A) Training Data Set (B)Testing Data Set.
Training set Data Set Web pages Session Requests
Cuboid 29 3798 9566
MsnBc 92 3234 12378
HyperReal 36 2567 8821
(A)
Testing set Data Set Web pages Session Requests
Cuboid 29 3471 7894
MsnBc 92 2931 9087
HyperReal 36 2130 7845
(B)
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Evaluation Parameters:
Various orders of Markov Model and Dynamic Nested Markov Model on three different datasets
have been evaluated. Evaluation parameters are Model Generation Time, Prediction Time,
Prediction Accuracy and Coverage.
(i)Model Generation Time
Generation time is defined as the time required by prediction model for modelling the training
file. Fig. 5 shows the model generation time of various order of Markov Model. Model generation
time is measured in millisecond. It is also observed that generation time depends on the number
of states generated by model. Second-order markov model generated more number of states as
compare to first order markov model so it take more time to generate.
Fig. 5: The time taken in millisecond for generation various order Model
(ii)Prediction Time
Fig. 6 shows prediction time taken by various order of model which is measured on the testing
file. The prediction time is depends on the other factor like the network traffic, server etc. But the
major time is of model which is observed in millisecond. The prediction time will be affected by
the number of web pages in the web log file also. The result shows that the prediction time will
increase with respect to number of web pages. The first-order Markov Model and DNMM take
same time to predict the next accessed web page. As move towards higher-order of Markov and
DNMM models, DNMM takes less prediction time as compared to Markov Model.
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Fig. 6: The prediction time taken in millisecond for various order of model.
(iii)Prediction Accuracy
Prediction accuracy is very important parameter for the prediction model. It measures the
accuracy of the prediction model applied for testing file and calculated as:
Prediction Accuracy = (Number of correct prediction) / (Number of test sessions)
Where number of correct prediction are the number of test user navigation sessions which are
correctly predicted and number of test sessions are the total number of test sessions on which
prediction is performed. Correct predictions are find out by comparing original and predicted web
pages, those predicted web pages which are equal to original web pages are consider as correct
prediction.
As shown in figure 7, first-order Markov Model and DNMM gives same prediction accuracy. As
move towards higher-order of Markov and DNMM models, DNMM gives high prediction
accuracy as compared to Markov Model.
Fig. 7: The prediction accuracy for various order models.
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(iv)Coverage
The coverage of the model is defined as the ratio of number of times model is able to predict to
number of requests in test set. In case of first-order Markov Model the coverage of state is 100%
and in second-order Markov Model the coverage of the state is 50% and as we move for the
higher order Markov Model the coverage will be less. For example in second-order Markov
Model where each state is a set of two web pages, if we want to predict that after web page
W1which will be the next web page accessed then model will fail to predict because it is not
having single state web page. In the DNMM in each order of the model, coverage will be 100%.
Conclusion
Various models have been analyzed in this work on the basis of Generation Time, Prediction Time
Prediction Accuracy and coverage. DNMM gives better prediction accuracy as compare to the
Markov Model. The coverage of the DNMM is better than the Markov Model.
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