Text preprocessing and document classification plays a vital role in web services discovery. Nearest centroid classifiers were mostly employed in high-dimensional application including genomics. Feature selection is a major problem in all classifiers and in this paper we propose to use an effective feature selection procedure followed by web services discovery through Centroid classifier algorithm. The task here in this problem statement is to effectively assign a document to one or more classes. Besides being simple and robust, the centroid classifier s not effectively used for document classification due to the computational complexity and larger memory requirements. We address these problems through dimensionality reduction and effective feature set selection before training and testing the classifier. Our preliminary experimentation and results shows that the proposed method outperforms other algorithms mentioned in the literature including K-Nearest neighbors, Naive Bayes classifier and Support Vector Machines.
Cluster based approach for Service Discovery using Pattern RecognitionYogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
Web Service Discovery Mechanisms Based on IR ModelsIRJET Journal
This document discusses various approaches for web service discovery that employ information retrieval (IR) methods. It describes five main approaches:
1. Using singular value decomposition (SVD) to find similar services by representing them as vectors and calculating cosine similarity.
2. Applying the vector space model of IR to represent services and queries as vectors and calculate cosine similarity to discover analogous services.
3. Combining the vector space model with a structure matching algorithm to refine service discovery results.
4. Measuring semantic similarity of services instead of structural similarity by representing data types as trees and calculating edit distances.
5. Enhancing service requests and descriptions with ontologies, representing them as vectors using latent semantic
Classification of web services using data mining algorithms and improved lear...TELKOMNIKA JOURNAL
As per the global digital report, 52.9% of the world population is using the internet, and 42% of
the world population is actively using e-commerce, banking, and other online applications. Web services
are software components accessed using networked communications and provide services to end users.
Software developers provide a high quality of web service. To meet the demands of user requirements,
it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands
of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset
and found important parameters are best practices, successability, availability, response time, reliability
and throughput, and compliance. We have used various data mining techniques and conducted
experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are
compared with various techniques random forest, artificial neural network, J48 decision tree, extreme
gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and
it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of
98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service
for mixed attributes.
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.
The document describes an ontology evolution process for classifying web services. It uses three techniques - TF/IDF, web context extraction, and free text descriptor verification - to analyze web service descriptions and automatically generate concepts and relationships for the ontology. TF/IDF and web context extraction are used to identify significant concepts from the descriptions. The free text descriptor is then used to validate these concepts and resolve any conflicts with the existing ontology. The combined approach aims to accurately define and evolve the ontology over time as new web services are added.
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
Query- And User-Dependent Approach for Ranking Query Results in Web DatabasesIOSR Journals
This document presents a new approach for ranking query results from web databases called Query and User Dependent Ranking. The approach considers two aspects: query similarity, which accounts for different users having similar queries, and user similarity, which accounts for different queries having similar user preferences. A prototype application was built to test the model. Empirical results found the approach to be efficient and suitable for real-world applications. The approach uses a workload file that is updated with ranking functions as new queries are made to track user and query similarities over time.
The semantic Web service discovery has been given massive attention within the last few years. With the
increasing number of Web services available on the web, looking for a particular service has become very
difficult, especially with the evolution of the clients’ needs. In this context, various approaches to discover
semantic Web services have been proposed. In this paper, we compare these approaches in order to assess
their maturity and their adaptation to the current domain requirements. The outcome of this comparison
will help us to identify the mechanisms that constitute the strengths of the existing approaches, and
thereafter will serve as guideline to determine the basis for a discovery approach more adapted to the
current context of Web services.
Cluster based approach for Service Discovery using Pattern RecognitionYogesh Santhan
Abstract— Web services that are appropriate to a user specific request are usually not considered in discovering the exact service since they are present without explicit related semantic descriptions. In our approach, we deal with the issue of service discovery provided non-explicit service description semantics that match a particular service request. We propose a system that involves semantic-based service categorization which is performed at the UDDI with a key for achieving the service categorization at functional level based on an ontology skeleton. Also, clustering is used for literally systemizing the web services based on functionality which is achieved by using analytic algorithm. An efficient matching for the relevant services is achieved by the enhancing the service request semantically and involves expanding the additional functionality (obtained from ontology) that are related for the requested service. The pattern recognition algorithm is used to select appropriate service from the cluster formation of related (grouped) web services.
Web Service Discovery Mechanisms Based on IR ModelsIRJET Journal
This document discusses various approaches for web service discovery that employ information retrieval (IR) methods. It describes five main approaches:
1. Using singular value decomposition (SVD) to find similar services by representing them as vectors and calculating cosine similarity.
2. Applying the vector space model of IR to represent services and queries as vectors and calculate cosine similarity to discover analogous services.
3. Combining the vector space model with a structure matching algorithm to refine service discovery results.
4. Measuring semantic similarity of services instead of structural similarity by representing data types as trees and calculating edit distances.
5. Enhancing service requests and descriptions with ontologies, representing them as vectors using latent semantic
Classification of web services using data mining algorithms and improved lear...TELKOMNIKA JOURNAL
As per the global digital report, 52.9% of the world population is using the internet, and 42% of
the world population is actively using e-commerce, banking, and other online applications. Web services
are software components accessed using networked communications and provide services to end users.
Software developers provide a high quality of web service. To meet the demands of user requirements,
it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands
of user measure service quality by the ranking of web services, in this paper, we analyzed QWS dataset
and found important parameters are best practices, successability, availability, response time, reliability
and throughput, and compliance. We have used various data mining techniques and conducted
experiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are
compared with various techniques random forest, artificial neural network, J48 decision tree, extreme
gradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and
it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of
98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service
for mixed attributes.
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.
The document describes an ontology evolution process for classifying web services. It uses three techniques - TF/IDF, web context extraction, and free text descriptor verification - to analyze web service descriptions and automatically generate concepts and relationships for the ontology. TF/IDF and web context extraction are used to identify significant concepts from the descriptions. The free text descriptor is then used to validate these concepts and resolve any conflicts with the existing ontology. The combined approach aims to accurately define and evolve the ontology over time as new web services are added.
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
Query- And User-Dependent Approach for Ranking Query Results in Web DatabasesIOSR Journals
This document presents a new approach for ranking query results from web databases called Query and User Dependent Ranking. The approach considers two aspects: query similarity, which accounts for different users having similar queries, and user similarity, which accounts for different queries having similar user preferences. A prototype application was built to test the model. Empirical results found the approach to be efficient and suitable for real-world applications. The approach uses a workload file that is updated with ranking functions as new queries are made to track user and query similarities over time.
The semantic Web service discovery has been given massive attention within the last few years. With the
increasing number of Web services available on the web, looking for a particular service has become very
difficult, especially with the evolution of the clients’ needs. In this context, various approaches to discover
semantic Web services have been proposed. In this paper, we compare these approaches in order to assess
their maturity and their adaptation to the current domain requirements. The outcome of this comparison
will help us to identify the mechanisms that constitute the strengths of the existing approaches, and
thereafter will serve as guideline to determine the basis for a discovery approach more adapted to the
current context of Web services.
This document provides a survey of semantic web personalization techniques. It begins by defining semantic web personalization and its advantages over traditional web personalization. It then classifies semantic web personalization approaches into several categories, including ontology-based, context-based, and hybrid recommendation systems. For each category, it provides examples of approaches and compares their methods and steps for personalization. The goal of the survey is to analyze and compare different techniques used for personalization in the semantic web.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
Integrated expert recommendation model for online communitiesst02IJwest
Online communities have become vital places for Web 2.0 users to share knowledg
e and experiences.
Recently, finding expertise user in community has become an important research issue. This paper
proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from
enormous contents and social network fe
atures. Vector space model is used to compute the relevance of
published content with respect
to a specific query while PageRank
algorithm is applied to rank candidate
experts. The experimental results sho
w that the proposed model is
an effective recommen
dation which can
guarantee that the most candidate experts are both highly relevant to the specific queries and highly
influential in corresponding areas
UML MODELING AND SYSTEM ARCHITECTURE FOR AGENT BASED INFORMATION RETRIEVALijcsit
In this current technological era, there is an enormous increase in the information available on web and
also in the online databases. This information abundance increases the complexity of finding relevant
information. To solve such challenges, there is a need for improved and intelligent systems for efficient
search and retrieval. Intelligent Agents can be used for better search and information retrieval in a
document collection. The information required by a user is scattered in a large number of databases. In this
paper, the object oriented modeling for agent based information retrieval system is presented. The paper
also discusses the framework of agent architecture for obtaining the best combination terms that serve as
an input query to the information retrieval system. The communication and cooperation among the agents
are also explained. Each agent has a task to perform in information retrieval.
IRJET- Review on Information Retrieval for Desktop Search EngineIRJET Journal
This document summarizes techniques for desktop search engines, including feature extraction using entity recognition, query understanding using part-of-speech tagging and segmentation, and similarity measures for scoring and ranking documents. It discusses using ontologies, concept graphs, semantic networks, and vector space models to represent knowledge in documents. Feature extraction identifies entities that can be mapped to knowledge bases to infer meanings. Query understanding aims to determine intent regardless of technique used. Similarity is measured using approaches like comparing maximum common subgraphs between a document and query graphs.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Tracing Requirements as a Problem of Machine Learning ijseajournal
This summary provides the key details from the document in 3 sentences:
The document discusses using a machine learning approach to classify traceability links between requirements. It proposes a 2-learner model that uses both lexical features from word pairs as well as features derived from a hand-built ontology. The model achieves a 56% reduction in error compared to a baseline using only lexical features, and performance is improved further by generating additional pseudo training instances from the ontology.
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
A new approach to gather similar operations extracted from web servicesIJECEIAES
A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
TWO WAY CHAINED PACKETS MARKING TECHNIQUE FOR SECURE COMMUNICATION IN WIRELES...pharmaindexing
This document discusses an efficient semantic data alignment approach based on fuzzy c-means (FCM) clustering to infer user search goals using feedback sessions. It aims to cluster similar pseudo-documents representing user feedback sessions to better understand user search intents for a given query. The approach first collects feedback sessions from search results and generates pseudo-documents. It then uses FCM clustering to group similar pseudo-documents while also measuring semantic similarity between terms. This is an improvement over k-means clustering which does not consider semantic similarity. The results are evaluated using metrics like classified average precision and show this FCM-based approach performs better than clustering without semantic alignment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
11.0004www.iiste.org call for paper.on demand quality of web services using r...Alexander Decker
This document summarizes a research paper on detecting duplicate records across multiple web databases. It proposes an unsupervised, online approach called UDD that uses two classifiers - Weighted Component Similarity Summing (WCSS) and Support Vector Machine (SVM) - in an iterative process to identify duplicate record pairs without requiring training data. The paper outlines the key components of the UDD approach, including how WCSS assigns weights to record fields, how duplicate records are identified based on similarity thresholds, and how the two classifiers cooperate in each iteration to find new duplicate pairs. Experimental results demonstrate the effectiveness of the proposed approach.
4.on demand quality of web services using ranking by multi criteria 31-35Alexander Decker
1) The document proposes an unsupervised, online approach called UDD for detecting duplicate records across query results from multiple web databases.
2) Two classifiers, weighted component similarity summing and support vector machines, are used cooperatively in an iterative process to identify duplicate record pairs without requiring labeled training data.
3) The approach assigns weights to record fields based on their similarity values in duplicate and non-duplicate record pairs, and uses a weighted sum of component similarities to determine if two records are duplicates.
User search goal inference and feedback session using fast generalized – fuzz...eSAT Publishing House
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
Since service-oriented architectures make the commercial systems more reliable and reusable, they have
gained more popularity in industry and scientific community in recent decades. Service-oriented
architectures bring flexibility and reusability to software design. Due to the increasing number of services
on the Web, finding a service which is suited to user requirements is crucial. The process of finding suitable
services to user request is one of the main purposes of service-oriented architectures. Many methods have
been proposed for service discovery in service-oriented architectures that try to fulfil user requirements
and offer suitable services to user request; however the proposed methods do not have enough precision for
discovering suitable services. In this paper, we propose a method for service discovery which offers more
accurate services according to user request. The proposed method is a hybrid semantic matchmaker for
service discovery in service oriented architecture. By providing accurate services suitable to user requests,
we have greatly increased the reusability rate and reduced the time and cost of software development
This document proposes a new method to re-rank web documents retrieved by search engines based on their relevance to a user's query using ontology concepts. It involves building an ontology of concepts for a given domain (electronic commerce), extracting concepts from retrieved documents, and re-ranking documents based on the frequency of ontology concepts within them. An evaluation showed the approach reduced average ranking error compared to search engines alone. The method was tested on the first 30 documents retrieved for the query "e-commerce" from search engines.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
Custom-Made Ranking in Databases Establishing and Utilizing an Appropriate Wo...ijsrd.com
Custom Rating System which provides a facility to the users, that they can search and download best articles or anything on the system in the database. The article or anything can be any text content which can describe a product, a book, an institution, an application, a company or anything. This system consists of two set of users, one is the normal user and another is the administrator. The users have to register and login to the system first, in order to use the system. The users have the following privileges. Write Article and Upload Relevant Files, Post Related URL to each article for other users reference, Search and Read Article posted by other users, Rate the articles posted by other users. The articles which are written by any user are sent to the Administrator for Approval. After approval of the articles by the administrator, they are available for the users to search and download. Based on the Description provided in an article, it can be searched by any registered user on the system. The user can see the article, download a file if available and the user can rate the article based on the article. Rating can be given in terms of 1 Star to 5 Star. The users can search the article. The list of articles displayed can be sorted based on following parameters: Rating, Popularity (Number of Clicks on the Article),Relevance (Based on number of matching keywords provided),All Articles uploaded by a specific user.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
WEB SERVICE DISCOVERY METHODS AND TECHNIQUES: A REVIEWijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
Web service discovery methods and techniques a reviewijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
This document provides a survey of semantic web personalization techniques. It begins by defining semantic web personalization and its advantages over traditional web personalization. It then classifies semantic web personalization approaches into several categories, including ontology-based, context-based, and hybrid recommendation systems. For each category, it provides examples of approaches and compares their methods and steps for personalization. The goal of the survey is to analyze and compare different techniques used for personalization in the semantic web.
IRJET- A Novel Technique for Inferring User Search using Feedback SessionsIRJET Journal
This document proposes a novel technique to infer user search goals using feedback sessions. It aims to address limitations in existing approaches like noisy search results, small numbers of clicked URLs, and lack of consideration of user feedback. The proposed approach generates feedback sessions from user click logs, pre-processes the data, extracts keywords from restructured results, re-ranks the results based on keywords and user history, and categorizes the re-ranked results using predefined categories. The technique is evaluated using Average Precision, which compares it to other clustering and classification algorithms. The goal is to improve information retrieval by better representing user search interests and needs.
Integrated expert recommendation model for online communitiesst02IJwest
Online communities have become vital places for Web 2.0 users to share knowledg
e and experiences.
Recently, finding expertise user in community has become an important research issue. This paper
proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from
enormous contents and social network fe
atures. Vector space model is used to compute the relevance of
published content with respect
to a specific query while PageRank
algorithm is applied to rank candidate
experts. The experimental results sho
w that the proposed model is
an effective recommen
dation which can
guarantee that the most candidate experts are both highly relevant to the specific queries and highly
influential in corresponding areas
UML MODELING AND SYSTEM ARCHITECTURE FOR AGENT BASED INFORMATION RETRIEVALijcsit
In this current technological era, there is an enormous increase in the information available on web and
also in the online databases. This information abundance increases the complexity of finding relevant
information. To solve such challenges, there is a need for improved and intelligent systems for efficient
search and retrieval. Intelligent Agents can be used for better search and information retrieval in a
document collection. The information required by a user is scattered in a large number of databases. In this
paper, the object oriented modeling for agent based information retrieval system is presented. The paper
also discusses the framework of agent architecture for obtaining the best combination terms that serve as
an input query to the information retrieval system. The communication and cooperation among the agents
are also explained. Each agent has a task to perform in information retrieval.
IRJET- Review on Information Retrieval for Desktop Search EngineIRJET Journal
This document summarizes techniques for desktop search engines, including feature extraction using entity recognition, query understanding using part-of-speech tagging and segmentation, and similarity measures for scoring and ranking documents. It discusses using ontologies, concept graphs, semantic networks, and vector space models to represent knowledge in documents. Feature extraction identifies entities that can be mapped to knowledge bases to infer meanings. Query understanding aims to determine intent regardless of technique used. Similarity is measured using approaches like comparing maximum common subgraphs between a document and query graphs.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Tracing Requirements as a Problem of Machine Learning ijseajournal
This summary provides the key details from the document in 3 sentences:
The document discusses using a machine learning approach to classify traceability links between requirements. It proposes a 2-learner model that uses both lexical features from word pairs as well as features derived from a hand-built ontology. The model achieves a 56% reduction in error compared to a baseline using only lexical features, and performance is improved further by generating additional pseudo training instances from the ontology.
Enhanced Retrieval of Web Pages using Improved Page Rank Algorithmijnlc
Information Retrieval (IR) is a very important and vast area. While searching for context web returns all
the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense
Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has
multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page
Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank
algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have
calculated Reciprocal Rank for both the algorithms and presented comparative results.
A new approach to gather similar operations extracted from web servicesIJECEIAES
A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
TWO WAY CHAINED PACKETS MARKING TECHNIQUE FOR SECURE COMMUNICATION IN WIRELES...pharmaindexing
This document discusses an efficient semantic data alignment approach based on fuzzy c-means (FCM) clustering to infer user search goals using feedback sessions. It aims to cluster similar pseudo-documents representing user feedback sessions to better understand user search intents for a given query. The approach first collects feedback sessions from search results and generates pseudo-documents. It then uses FCM clustering to group similar pseudo-documents while also measuring semantic similarity between terms. This is an improvement over k-means clustering which does not consider semantic similarity. The results are evaluated using metrics like classified average precision and show this FCM-based approach performs better than clustering without semantic alignment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
11.0004www.iiste.org call for paper.on demand quality of web services using r...Alexander Decker
This document summarizes a research paper on detecting duplicate records across multiple web databases. It proposes an unsupervised, online approach called UDD that uses two classifiers - Weighted Component Similarity Summing (WCSS) and Support Vector Machine (SVM) - in an iterative process to identify duplicate record pairs without requiring training data. The paper outlines the key components of the UDD approach, including how WCSS assigns weights to record fields, how duplicate records are identified based on similarity thresholds, and how the two classifiers cooperate in each iteration to find new duplicate pairs. Experimental results demonstrate the effectiveness of the proposed approach.
4.on demand quality of web services using ranking by multi criteria 31-35Alexander Decker
1) The document proposes an unsupervised, online approach called UDD for detecting duplicate records across query results from multiple web databases.
2) Two classifiers, weighted component similarity summing and support vector machines, are used cooperatively in an iterative process to identify duplicate record pairs without requiring labeled training data.
3) The approach assigns weights to record fields based on their similarity values in duplicate and non-duplicate record pairs, and uses a weighted sum of component similarities to determine if two records are duplicates.
User search goal inference and feedback session using fast generalized – fuzz...eSAT Publishing House
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
Since service-oriented architectures make the commercial systems more reliable and reusable, they have
gained more popularity in industry and scientific community in recent decades. Service-oriented
architectures bring flexibility and reusability to software design. Due to the increasing number of services
on the Web, finding a service which is suited to user requirements is crucial. The process of finding suitable
services to user request is one of the main purposes of service-oriented architectures. Many methods have
been proposed for service discovery in service-oriented architectures that try to fulfil user requirements
and offer suitable services to user request; however the proposed methods do not have enough precision for
discovering suitable services. In this paper, we propose a method for service discovery which offers more
accurate services according to user request. The proposed method is a hybrid semantic matchmaker for
service discovery in service oriented architecture. By providing accurate services suitable to user requests,
we have greatly increased the reusability rate and reduced the time and cost of software development
This document proposes a new method to re-rank web documents retrieved by search engines based on their relevance to a user's query using ontology concepts. It involves building an ontology of concepts for a given domain (electronic commerce), extracting concepts from retrieved documents, and re-ranking documents based on the frequency of ontology concepts within them. An evaluation showed the approach reduced average ranking error compared to search engines alone. The method was tested on the first 30 documents retrieved for the query "e-commerce" from search engines.
An Effective Approach for Document Crawling With Usage Pattern and Image Base...Editor IJCATR
As the Web continues to grow day by day each and every second a new page gets uploaded into the web; it has become
a difficult task for a user to search for the relevant and necessary information using traditional retrieval approaches. The amount of
information has increased in World Wide Web, it has become difficult to get access to desired information on Web; therefore it
has become a necessity to use Information retrieval tools like Search Engines to search for desired information on the Internet or
Web. Already Existing and used Crawling, Indexing and Page Ranking techniques that are used by the underlying Search Engines
before the result gets generated, the result sets that are returned by the engine lack in accuracy, efficiency and preciseness. The
return set of result does not really satisfy the request of the user and results in frustration on the user’s side. A Large number of
irrelevant links/pages get fetched, unwanted information, topic drift, and load on servers are some of the other issues that need to
be caught and rectified towards developing an efficient and a smart search engine. The main objective of this paper is to propose
or present a solution for the improvement of the existing crawling methodology that makes an attempt to reduce the amount of load
on server by taking advantage of computational software processes known as “Migrating Agents” for downloading the related
pages that are relevant to a particular topic only. The downloaded Pages are then provided a unique positive number i.e. called the
page has been ranked, taking into consideration the combinational words that are synonyms and other related words, user
preferences using domain profiles and the interested field of a particular user and past knowledge of relevance of a web page that
is average amount of time spent by users. A solution is also been given in context to Image based web Crawling associating the
Digital Image Processing technique with Crawling.
Custom-Made Ranking in Databases Establishing and Utilizing an Appropriate Wo...ijsrd.com
Custom Rating System which provides a facility to the users, that they can search and download best articles or anything on the system in the database. The article or anything can be any text content which can describe a product, a book, an institution, an application, a company or anything. This system consists of two set of users, one is the normal user and another is the administrator. The users have to register and login to the system first, in order to use the system. The users have the following privileges. Write Article and Upload Relevant Files, Post Related URL to each article for other users reference, Search and Read Article posted by other users, Rate the articles posted by other users. The articles which are written by any user are sent to the Administrator for Approval. After approval of the articles by the administrator, they are available for the users to search and download. Based on the Description provided in an article, it can be searched by any registered user on the system. The user can see the article, download a file if available and the user can rate the article based on the article. Rating can be given in terms of 1 Star to 5 Star. The users can search the article. The list of articles displayed can be sorted based on following parameters: Rating, Popularity (Number of Clicks on the Article),Relevance (Based on number of matching keywords provided),All Articles uploaded by a specific user.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
WEB SERVICE DISCOVERY METHODS AND TECHNIQUES: A REVIEWijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
Web service discovery methods and techniques a reviewijcseit
Web Services are independent software systems which offer machine-to-machine interactions over the
Internet to achieve well-described operations. With the advent of Service-Oriented Architecture (SOA),
Web Services have gained tremendous popularity. As the number of Web Services is increased, finding the
best service according to users requirements becomes a challenge. The Semantic Web Service discovery is
the process of finding the most suitable service that satisfies the user request. A number of approaches to
Web Service discovery have been proposed. In this paper, we classify them and determine the advantages
and disadvantages of each group, to help researchers to implement a new or to select the most appropriate
existing approach for Semantic Web Service discovery. We, also, provide a taxonomy which categorizes
Web Service discovery systems from different points of view. There are three different views, namely,
architectural view, automation view and matchmaking view. We focus on the matchmaking view which is
further divided into semantic-based, syntax-based and context-aware. We explain each sub-group of it in
detail, and then subsequently compare the sub-groups in terms of their merits and drawbacks.
Web Services Discovery and Recommendation Based on Information Extraction and...ijwscjournal
This paper shows that the problem of web services representation is crucial and analyzes the various
factors that influence on it. It presents the traditional representation of web services considering traditional
textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services
descriptions are dirty and need significant cleaning to keep only useful information. To deal with this
problem, we introduce rules based text tagging method, which allows filtering web service description to
keep only significant information. A new representation based on such filtered data is then introduced.
Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called
symbolic reputation, which is computed from relationships between web services. The impact of the use of
these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of web service composition methods and techniques. It begins with an abstract that discusses the increasing need for web service composition as more services become available online. The document then reviews several key concepts related to web service composition, such as ontologies, semantic annotations, quality of service measures, and description languages. It categorizes composition approaches as either syntactic or semantic-based. Syntactic composition relies on syntax alone while semantic composition leverages semantic descriptions. The document discusses various composition methods including manual/static composition, automatic/dynamic composition, and semi-automatic/dynamic or static composition. It provides examples of techniques used for each method. Overall, the document aims to help researchers focus their efforts on developing lasting solutions for
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Web Services are modular, self-describing, self-contained and loosely coupled applications that can be
published, located, and invoked across the web. With the increasing number of web services available on
the web, the need for web services composition is becoming more and more important. Nowadays, for
answering complex needs of users, the construction of new web services based on existing ones is required.
This problem is known as web services composition. However, it is one of big challenge problems of recent
years in a distributed and dynamic environment. The various approaches in field of web service
compositions proposed by the researchers. In this paper we present a review of existing approaches for
web service composition and compare them among each other with respect to some key requirements. We
hope this paper helps researchers to focus on their efforts and to deliver lasting solutions in this field.
IRJET- A Parameter Based Web Service Discovery with Underlying Semantics ProfileIRJET Journal
1. The document discusses various approaches for web service discovery. It describes syntax-based and semantic-based approaches.
2. It outlines several specific approaches for web service discovery including one based on schema matching that uses semantic annotations to enhance discovery. Another approach uses a neural network to calculate semantic similarities between schemas to improve matching.
3. A third approach discusses goal-oriented discovery of resources in RESTful architectures using three layers of abstraction from the agent level to service level to content level.
4. Finally, it introduces the concept of linked services for the web of data by publishing service annotations as linked data and creating services that process and generate linked data.
WEB SERVICES COMPOSITION METHODS AND TECHNIQUES: A REVIEWijcseit
This document provides a review of existing approaches for web service composition. It begins with an introduction and background on key concepts related to web services and composition such as ontologies, semantic web architecture, semantic annotations, reasoners, matching, and quality of service. It then discusses current methods for web service composition, distinguishing between manual/static composition done at design time versus automatic/dynamic composition done at runtime. The document categorizes and compares different composition approaches and aims to help focus future research efforts.
Three Dimensional Database: Artificial Intelligence to eCommerce Web service ...CSCJournals
A main objective of this paper is using artificial intelligence technique to web service agents and increase the efficiency of the agent communications. In recent years, web services have played a major role in computer applications. Web services are essential, as the design model of applications are dedicated to electronic businesses. This model aims to become one of the major formalisms for the design of distributed and cooperative applications in an open environment (the Internet). Current commercial and research-based efforts are reviewed and positioned within these two fields. A web service as a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-process able format (specifically Web Services Description Language WSDL). Other systems interact with the web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web-related standards. Particular attention is given to the application of AI techniques to the important issue of WS composition. Within the range of AI technologies considered, we focus on the work of the Semantic Web and Agent-based communities to provide web services with semantic descriptions and intelligent behavior and reasoning capabilities. Re-composition of web services is also considered and a number of adaptive agent approaches are introduced and implemented in publication domain with three dimensional databases and one of the areas of work is eCommerce.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
The document discusses automatic data unit annotation in search results. It proposes a method that clusters data units on result pages into groups containing semantically similar units. Then, multiple annotators are used to predict annotation labels for each group based on features of the units. An annotation wrapper is constructed for each website to annotate new result pages from that site. The method aims to improve search response by providing meaningful annotations of data units within results. It is evaluated based on precision and recall for the alignment of data units and text nodes during the annotation process.
WEB SERVICES DISCOVERY AND RECOMMENDATION BASED ON INFORMATION EXTRACTION AND...ijwscjournal
This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the
WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the
experimentation using real world web services.
Projection Multi Scale Hashing Keyword Search in Multidimensional DatasetsIRJET Journal
The document discusses a novel method called ProMiSH (Projection and Multi Scale Hashing) for keyword search in multi-dimensional datasets. ProMiSH uses random projection and hash-based index structures to achieve high scalability and speedup of more than four orders over state-of-the-art tree-based techniques. Empirical studies on real and synthetic datasets of sizes up to 10 million objects and 100 dimensions show ProMiSH scales linearly with dataset size, dimension, query size, and result size. The method groups objects embedded in a vector space that are tagged with keywords matching a given query.
SERVICE SELECTION BASED ON DOMINANT ROLE OF THE CHOREOGRAPHYijwscjournal
Web services are playing dominant role on Internet for e-business. The compositions of these services are used to meet business objectives. The web service choreography describes the external observable behavior
of these compositions. Many compositions may available for same functionality. These compositions cannot be distinguished on the basis of functional properties. This Quality of services (QoS) may help the user to select web services and to analyze composition of the web services. Web service choreography is going to dictate implementation of workflow. This workflow consists of several tasks. Each task is implemented by web services. These services are hosted in large numbers by different service providers on different service clusters. The mapping of service and task is difficult issue in run time environment. The interoperability between services is also a great problem. The selection of services is very big issue. In this paper we have proposed a bio-inspired selection algorithm based on dominant role and proposed a discovery infrastructure. We have also used the client behavior to improve the failure of the composition of the service.
SERVICE SELECTION BASED ON DOMINANT ROLE OF THE CHOREOGRAPHY ijwscjournal
Web services are playing dominant role on Internet for e-business. The compositions of these services are
used to meet business objectives. The web service choreography describes the external observable behavior
of these compositions. Many compositions may available for same functionality. These compositions cannot
be distinguished on the basis of functional properties. This Quality of services (QoS) may help the user to
select web services and to analyze composition of the web services. Web service choreography is going to
dictate implementation of workflow. This workflow consists of several tasks. Each task is implemented by
web services. These services are hosted in large numbers by different service providers on different service
clusters. The mapping of service and task is difficult issue in run time environment. The interoperability
between services is also a great problem. The selection of services is very big issue.
SERVICE SELECTION BASED ON DOMINANT ROLE OF THE CHOREOGRAPHY ijwscjournal
Web services are playing dominant role on Internet for e-business. The compositions of these services are
used to meet business objectives. The web service choreography describes the external observable behavior
of these compositions. Many compositions may available for same functionality. These compositions cannot
be distinguished on the basis of functional properties. This Quality of services (QoS) may help the user to
select web services and to analyze composition of the web services. Web service choreography is going to
dictate implementation of workflow. This workflow consists of several tasks. Each task is implemented by
web services. These services are hosted in large numbers by different service providers on different service
clusters. The mapping of service and task is difficult issue in run time environment. The interoperability
between services is also a great problem. The selection of services is very big issue.
SERVICE SELECTION BASED ON DOMINANT ROLE OF THE CHOREOGRAPHYijwscjournal
Web services are playing dominant role on Internet for e-business. The compositions of these services are used to meet business objectives. The web service choreography describes the external observable behavior of these compositions. Many compositions may available for same functionality. These compositions cannot be distinguished on the basis of functional properties. This Quality of services (QoS) may help the user to select web services and to analyze composition of the web services. Web service choreography is going to dictate implementation of workflow. This workflow consists of several tasks. Each task is implemented by web services. These services are hosted in large numbers by different service providers on different service clusters. The mapping of service and task is difficult issue in run time environment. The interoperability between services is also a great problem. The selection of services is very big issue. In this paper we have proposed a bio-inspired selection algorithm based on dominant role and proposed a discovery infrastructure. We have also used the client behavior to improve the failure of the composition of the service.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
An Improved Support Vector Machine Classifier Using AdaBoost and Genetic Algo...Eswar Publications
Predicting the objective of internet users has divergent applications in the areas such as e-commerce, entertainment in online, and several internet-based applications. The critical part of the classifying internet queries based on obtainable features namely contextual information, keywords and their semantic relationships. This research paper presents an improved support vector machine classifier that makes use of ad boost genetic algorithmic approach towards web interaction mining. Around 31 participants are chosen and given topics to
search web contents. Parameters such as precision, recall and F1 score are taken for comparing the proposed classifier with the classical support vector machine. Results proved that the proposed classifier achieves better performance than that of the conventional SVM.
AGENTS AND OWL-S BASED SEMANTIC WEB SERVICE DISCOVERY WITH USER PREFERENCE SU...IJwest
Service-oriented computing (SOC) is an interdisciplinary paradigm that revolutionizes the very fabric of
distributed software development applications that adopt service-oriented architectures (SOA) can evolve
during their lifespan and adapt to changing or unpredictable environments more easily. SOA is built
around the concept of Web Services. Although the Web services constitute a revolution in Word Wide Web,
they are always regarded as non-autonomous entities and can be exploited only after their discovery. With
the help of software agents, Web services are becoming more efficient and more dynamic.
The topic of this paper is the development of an agent based approach for Web services discovery and
selection in witch, OWL-S is used to describe Web services, QoS and service customer request. We develop
an efficient semantic service matching which takes into account concepts properties to match concepts in
Web service and service customer request descriptions. Our approach is based on an architecture
composed of four layers: Web service and Request description layer, Functional match layer, QoS
computing layer and Reputation computing layer.
Similar to 24 24 nov16 13455 27560-1-sm(edit) (20)
This document describes an electronic doorbell system that uses a keypad and GSM for home security. The system consists of a doorbell connected to a microcontroller that triggers a GSM module to send an SMS to the homeowner when the doorbell is pressed. The homeowner can then respond via button press to open the door, or a message will be displayed if they do not respond. An authorized person can enter a password on the keypad, and if multiple wrong passwords are entered, a message will be sent to the homeowner about a potential burglary attempt. The system aims to provide notification to homeowners and prevent unauthorized access through use of passwords and messaging capabilities.
Augmented reality, the new age technology, has widespread applications in every field imaginable. This technology has proven to be an inflection point in numerous verticals, improving lives and improving performance. In this paper, we explore the various possible applications of Augmented Reality (AR) in the field of Medicine. The objective of using AR in medicine or generally in any field is the fact that, AR helps in motivating the user, making sessions interactive and assist in faster learning. In this paper, we discuss about the applicability of AR in the field of medical diagnosis. Augmented reality technology reinforces remote collaboration, allowing doctors to diagnose patients from a different locality. Additionally, we believe that a much more pronounced effect can be achieved by bringing together the cutting edge technology of AR and the lifesaving field of Medical sciences. AR is a mechanism that could be applied in the learning process too. Similarly, virtual reality could be used in the field where more of practical experience is needed such as driving, sports, neonatal care training.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
In the wake of the sudden replacement of wood and kerosene by gas cookers for several purposes in Nigeria, gas leakage has caused several damages in our homes, Laboratories among others. installation of a gas leakage detection device was globally inspired to eliminate accidents related to gas leakage. We present an alternative approach to developing a device that can automatically detect and control gas leakages and also monitor temperature. The system detects the leakage of the LPG (Liquefied Petroleum Gas) using a gas sensor, then triggred the control system response which employs ventilator system, Mobile phone alert and alarm when the LPG concentration in the air exceeds a certain level. The performance of two gas sensors (MQ5 and MQ6) were tested for a guided decision. Also, when the temperature of the environment poses a danger, LED (indicator), buzzer and LCD (16x2) display was used to indicate temperature and gas leakage status in degree Celsius and PPM respectively. Attension was given to the response time of the control system, which was ascertained that this system significantly increases the chances and efficiency of eliminating gas leakage related accident.
Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.
The document proposes the Gentoo Penguin Algorithm (GPA) to solve the optimal reactive power problem. The goal is to minimize real power loss. GPA is inspired by the natural behaviors of Gentoo penguins. In GPA, penguin positions represent potential solutions. Penguins move toward other penguins with lower "cost" or higher heat concentration, representing better solutions. Cost is defined by heat concentration and distance between penguins. Heat radiation decreases with distance. The algorithm is tested on the IEEE 57 bus system and reduces real power loss effectively compared to other methods.
08 20272 academic insight on applicationIAESIJEECS
This research has thrown up many questions in need of further investigation.There was an expressive quantitative-qualitative research, which a common investigation form was used in.The dialogue item was also applied to discover if the contributors asserted the media-based attitude supplements their learning of academic English writing classes or not.Data recounted academic” insights toward using Skype as a sustaining implement for lessons releasing based on chosen variables: their occupation, year of education, and knowledge with Skype discovered that there were no important statistical differences in the use of Skype units because of medical academics major knowledge. There are statistically important differences in using Skype units. The findings also, disclosed that there are statistically significant differences in using Skype units due to the practice with Skype variable, in favors of academics with no Skype use practice. Skype instrument as an instructive media is a positive medium to be employed to supply academic medical writing data and assist education. Academics who do not have enough time to contribute in classes believe comfortable using the Skype-based attitude in scientific writing. They who took part in the course claimed that their approval of this media is due to learning academic innovative medical writing.
Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision-Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.
Notary is an official authorized to make an authentic deed regarding all deeds, agreements and stipulations required by a general rule. Activities carried out at the notary office such as recording client data and file data still use traditional systems that tend to be manual. The problem that occurs is the inefficiency in data processing and providing information to clients. Clients have difficulty getting information related to the progress of documents that are being taken care of at the notary's office. The client must take the time to arrive to the notary's office repeatedly to check the progress of the work of the document file. The purpose of this study is to facilitate clients in obtaining information about the progress of the work in progress, and make it easier for employees to process incoming documents by implementing an administrative system. This system was developed with the waterfall system development method and uses the Multi-Channel Access Technology integrated in the website to simplify the process of delivering information and requesting information from clients and to clients with Telegram and SMS Gateway. Clients will come to the office only when there is a notification from the system via Telegram or SMS notifying that the client must come directly to the notary's office, thus leading to an efficient time and avoiding excessive transportation costs. The overall functional system can function properly based on the results of alpha testing. The results of beta testing conducted by distributing the system feasibility test questionnaire to end users, get a percentage of 96% of users agree the system is feasible to be implemented.
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians-both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication-based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore, to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
Levelised Cost of Hydrogen (LCOH) Calculator ManualMassimo Talia
The aim of this manual is to explain the
methodology behind the Levelized Cost of
Hydrogen (LCOH) calculator. Moreover, this
manual also demonstrates how the calculator
can be used for estimating the expenses associated with hydrogen production in Europe
using low-temperature electrolysis considering different sources of electricity
Tools & Techniques for Commissioning and Maintaining PV Systems W-Animations ...Transcat
Join us for this solutions-based webinar on the tools and techniques for commissioning and maintaining PV Systems. In this session, we'll review the process of building and maintaining a solar array, starting with installation and commissioning, then reviewing operations and maintenance of the system. This course will review insulation resistance testing, I-V curve testing, earth-bond continuity, ground resistance testing, performance tests, visual inspections, ground and arc fault testing procedures, and power quality analysis.
Fluke Solar Application Specialist Will White is presenting on this engaging topic:
Will has worked in the renewable energy industry since 2005, first as an installer for a small east coast solar integrator before adding sales, design, and project management to his skillset. In 2022, Will joined Fluke as a solar application specialist, where he supports their renewable energy testing equipment like IV-curve tracers, electrical meters, and thermal imaging cameras. Experienced in wind power, solar thermal, energy storage, and all scales of PV, Will has primarily focused on residential and small commercial systems. He is passionate about implementing high-quality, code-compliant installation techniques.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
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442
Web service as illustrated in Figure 1 describes a standard way of web based
applications using different internet protocol backbone including XML which is to tag data,
SOAP to transfer data, WSDL to describe services and UDDI to list what services are available.
Defining a web service’s domain is needed for web service discovery, composition and
semantic annotation. Hence we studied different classification algorithms and identified the best
one for web services discovery along with the idea of feature set selection from the data sets
used for training and testing. We find that effective feature set selection followed by centroid
classifier algorithm works wells for web services discovery when compared with the traditional
algorithms.
2. Literature Survey
Data classification has got a wide variety of applications as it tries to distinguish or find
the relationship between the feature variables and the target variables of interest. There are two
types of classification algorithms – namely the supervised classification and the unsupervised
classification. The former refers to the machine learning job of understanding a task from
labeled training data while the latter describe hidden structure from unlabeled data. There are
multiple classification algorithms available in the literature as discussed below and we find the
optimal one along with our novel approach that is best suitable for web services discovery.
Ximing Li, Jihong Ouyang and Xiaotang Zhou [1] has discussed about document
classification in their research paper. They feel that the native algorithm suffers from over-fitting
and linear inseparability problems and to overcome they have proposed a kernel-based
hypothesis margin centroid classifier algorithm.
Mamoun M., et al., [2] proposed a generic non-functional based web services discovery
classification algorithm. They have the results proven mathematically and also experimentally.
George Potamias [3] and his team have enhanced web services by combining document
classification along with the user profile. They have demonstrated the effectiveness of their
proposed approach through experimental results.
V. Vineeth Kumar and Dr. N Satyanarayana [4] feel that majority of the web services are
missing precise description. To overcome the short comings, they have proposed a self-
adaptive semantic classification method by means of service data ontology and web user log
frequent patterns. Marco Crasso, et al., [5] has described that semantic web services
technologies are not widely adopted due to the efforts required in annotating semantically
ordinary services. They have combined text preprocessing, document classification and
ontology alignment techniques to extract information from standard service descriptions.
Hongbing Wang along with others [6] have found that the present methods for web
service discovery study only for small data set. They have proposed to use a new feature
selection method for better classification and their results were convincing as well.
K. Venkatachalam, N. K. Karthikeyan and S. Lavanya [7] has described the need for
machine understandability. They have derived an approach for web services descriptions based
on the informal user request made in a natural language. E. Karakoc and P. Senkul [8] has
described in detail about web composition approach in which a rich set of restraints can be well-
defined on the composite service.
Tamer A. Farrag and others [9] feel that the matchmaking between the user requests
and semantic web services is the main function in web services discovery mechanism. They
have proposed a semantic distance based algorithm for the same.
Adala A., and Tabbane N., [10] detailed that to promote the automation of web services
discovery, multiple semantic languages have been created which allows to describe the
services functionality in a form understood by the machine using semantic web technologies.
They have also provided a solution using discovery framework which enables web services
discovery based on keywords in natural language.
Ankolekar, et al., [11] presented DAML-S, ontology for detailing the properties of web
services. They provide web services descriptions at the application layer and details what a
service can do. They focus on the grounding which connects ontology with XML based
descriptions of the web services.
Jyotishman Pathak and others [12] described a framework for ontology based flexible
discovery of web services. They convert user queries in to queries that can be processed by a
matchmaking engine that knows relevant domain ontologies and web services.
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443
Ramakanta Mohanty and others [13] have employed different classification algorithms
for document classification including the neural networks, classification and regression trees etc.
They have carried out their experiments on QWS data set and reported their findings for web
services classification.
Jyotishman Pathak, et al., [14] have discussed about web services provider and
consumer number increase along with the need for effective web services classification tool.
Their tool automatically selects a service through filtering and certain user requirements. In the
second step they classify the services using formal concept analysis approach and also keep
track of the items selected by the user for future usage.
Tamer A. Farrag and team [15] have found a semantic distance based matchmaking
algorithm and measured the distance among the user request and the web service tested as a
pointer of the degree of relevance amongst them. They have a deep evaluation process carried
out to validate this approach as well.
3. Design
Machine learning algorithms build systems that learn from experience. From web
services discovery point of view, the examples could be:
a) Vendor interested in knowing consumer group to target for selling the products
b) Doctor interested in identifying the root cause for a particular disease
c) Fans interested in retrieving the pictures of their film stars etc. to name a few.
Classification algorithms are hence needed to study these requirements and come up
with a model for prediction. Typically they contain two phases including the training phase and
the testing phase. During the training stage, a model is constructed from the training vectors
and during the testing phase, the described model will classify the test instance in to one of the
trained classes. Data classification can be solved using multiple techniques including decision
trees, neural networks, Support Vector Machines (SVM), nearest neighbor and probabilistic
methods.
Web services classification can be solved according to the domain considering both the
textual description and the semantic annotations. It is a twostep process including the keywords
retrieval and the document classification. Though there are multiple algorithms available for
document classification, the best ones are discussed in this paper along with our solution for
effective web services discovery.
Figure 2. Data Classification flow diagram
Feature selection is the first phase of most classification algorithms as shown in Figure
2. We propose a new approach in feature set selection followed by discussion on three different
classification algorithms including the KNN algorithm, Centroid classifier algorithm and Support
Vector Machines.
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For supervised learning, the following steps needs to be performed. Training data needs
to be identified first. A set of input and output objects are gathered either from experts or from
measurements. The input objects are converted in to feature vectors and a subset is selected
for training. The learning algorithm is then selected and deployed with the identified subset of
feature vectors. Run the learning algorithm on the training set and get the required output.
Accuracy of the learned function is evaluated and then tested with the new set of test vectors.
The classification error of a learning algorithm is linked to the sum of the bias and the
variance of the system. Typically, there is a compromise between bias and variance. A
classification algorithm with low bias must be flexible to fit the data well. Dimensionality of the
training vectors is a major issue and we address the same in the following sections along with
the best classification algorithm for web services discovery.
3.1. K – Nearest Neighbor Algorithm
KNN, expanded as K – Nearest Neighbor Algorithm in pattern recognition is the
simplest and most commonly used algorithm for classification. KNN also is deployed at places
for estimation and prediction. People also regard it as lazy learning algorithm. KNN is very
simple to understand and is having a very vast range of applications. KNN is also referred as
instance based learning. The training data set is stowed; the classification for the unclassified
record can be achieved by just comparing it to the similar records available in the data set. This
algorithm is referred as lazy as it does not make use of the training data points to carry out any
generalization. This can also be conveyed as the training phase is non-existent or very
negligible. This enables the training phase to be very fast. Since the training is non-existent or
very minimal, it has an impact in the time one should spend on testing. The testing phase would
need a lot of time and also memory.
Whenever there is a need to classify a new point, one would find its K nearest
neighbors from the training data set, adhering very much the name. The distance is normally
calculated with one of the following methods as:
a. Euclidean Distance
b. Minkowski Distance
c. Mahalanobis Distance
A simple example is taken as an instance to explain the way k-nearest neighbor’s
algorithm works, below.
Assume that the query instance is Xp, Let X1, X2, X3, X4 …. XT signify the T instances
from the training examples that are in the closer vicinity to Xp. The return would be in such a
way that the class represents the maximum of the T instances. Figure 3 represents the KNN
instance along with the scenario discussed above.
Assuming T = 6, taking this scenario as shown in above example, the case query
instance Xp would be classified as negative. This is based on the simple rule that the
classification should be based on nearest neighbors. Here, in this case, four of its nearest
neighbors are negative and hence Xp shall also be getting classified as negative, leaving
behind positive.
Figure 3. KNN – An Instance
The distance is the key here and it relates to all the features. All the features or
attributes are assumed to have the same impact on the distance. While using KNN one should
also be informed about the curse of dimensionality. If the classification is carried out wrong
which is predominantly due to the presence of irrelevant attributes is defined as curse of the
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dimensionality. For multi-class k-NN classification algorithm, Cover and Hart (1967) prove an
upper bound error rate of:
R* <= RKNN <= R*(2 – MR* / (M – 1))
Where R* represents the Bayes error rate, RKNN is the k-NN error rate, and M corresponds to
the number of classes in the dataset. For M=2, as we see that the Bayesian error rate R*
approaches close to zero, this limit decreases to "not more than twice the Bayesian error rate".
Though KNN appears to be good for classification, the main disadvantage is that it is a
lazy learner. It uses the training data itself for prediction rather than learning from it. This will
slow down the prediction time when the data set is higher. Also the algorithm does not get
generalized well and the robustness to noise is also not good. For these reasons, we study the
next algorithm called Centroid classifier and use it in web services discovery.
3.2. Centroid Classifier Algorithm
The Centroid of a plane figure is the average of all the data points in the shape. It can
be extended to n-dimensional space as well. Automatic text categorization is an important task
for us in web services discovery and linear-time centroid based classification algorithm would be
best suited for this application due to its simplicity and robust performance.
In data retrieval, ‘tf-idf’ represents term frequency-inverse document frequency which is
a measure of importance of a word in a document. It increases proportionately to the number of
times a word is present in the text. One of the technique used in data mining to measure
cohesion within clusters is the cosine similarity. It measures the orientation and not the
magnitude which makes sure that we are not just interested in the ‘tf-idf’ but the angle between
the documents. Cosine similarity of 1 indicates same orientation while -1 indicates diametrically
opposed ones.
During the training phase of the algorithm, if the labeled training samples are {(x1,
y1),…,(xn, yn)} with class labels yi € Y, then we compute the centroids per class as follows:
µ = (1/cl) ∑ xi (1)
Where c is the set of indices of samples belonging to that particular class.
During the testing phase of the algorithm, we identify the class of the test vector x as
follows:
Y = arg min ║µ - x║ (2)
Where y is the predicted output and µ corresponds to the centroids computed. Figure 4
represents the Nearest Centroid classifier.
Figure 4. Nearest Centroid Classifier
For web services discovery, the documents are first represented in the vector space
before proceeding with the calculations. In other words, every document is denoted by the term
frequency values dtf = {tf1, tf2,…,tfn}. The similarity or the distance between the two documents
is computed using the cosine function as follows:
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cos (di, dj) = di.dj / (║di║ * ║dj║ (3)
Where “.” means the dot product of the two vectors from the different documents.
The centroid classifier algorithm is not complex to compute when compared to most
traditional algorithms and in learning phase, it is linear. When a new test vector comes, the
amount of time required to find the type of class is O(km) where m represents the number of
terms present in the new document x.
3.3. Feature Set Selection for Training
Feature selection is the first step of all classification algorithms. Most time data is
collected by non-domain experts and some of them could be irrelevant. These noisy data could
result in poor modelling and should be eradicated. Feature selection could be made either
through filtering models or through wrapper models. Filtering model selects the subset using
intrinsic characteristics of the data while the wrapper model requires an algorithm to find the
feature subset. We propose to use a new algorithm for wrapper model and then combine both
the filter and wrapper model as shown in Figure 5 for feature subset selection making it a new
hybrid model approach in web services discovery.
Figure 5. SVM – Hybrid model in feature subset selection
For filter approach, features could be selected based on the statistical properties. They
perform two operations including the ranking and subset selection. In ranking, each feature is
evaluated without considering the interactions among the other elements of the set and then the
subset to be selected is provided. Sometimes filter methods can select the redundant or the
noisy variables as well as they do not consider any relationships or models between variables
during selection. For these reasons, we go for the next method called wrapper method as the
second pass of selecting the feature subset.
The relation between the feature subset selection and the relevance is an important
factor in wrapper method. So, in wrapper method, we start with the first input vector and keep
adding the features one at a time and test it immediately with the user relevance in the past. If it
is relevant, then we add it on feature subset otherwise not. As per the centroid classifier
algorithm discussed, the centroids gets adjusted every time a document gets added or
removed. This happens regularly during training phase and does not happen frequently during
testing.
If there are “N” documents present originally in the set and when a new document (a,b)
gets added, then the original centroid (x,y) gets updated as follows:
Centroid = ((Nx + a) / (N + 1), (Ny + b) / (N + 1)) (4)
In the similar way, when a document is found to be irrelevant it can be removed from
the feature set selected as follows:
Centroid = ((Nx - a) / (N - 1), (Ny - b) / (N - 1)) (5)
The wrapper approach is better in terms of performances and at the same time
requiring greater computational resources. To avoid this issue, we use the filer method first to
reduce the original set from a huge size to a medium size and then deploy the wrapper method
on the reduced set for further feature subset selection making both the performance and
resource usage better. Feature selection and feature extraction together helps in dimensionality
reduction hence.
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3.4. Support Vector Machines Based Classification
Support Vector Machines (SVM) are supervised learning models with related learning
procedures that explores data used for classification and regression analysis. From the subset
selected each marked with one of two categories, SVM algorithm builds a model that assigns
test vectors into one class or the other. This is linear classification and SVM is capable of non-
linear classification as well just by mapping inputs into high-dimensional feature space through
kernel trick.
Support Vector Machines constructs a hyperplane in a high dimensional space which
can be used in classification problems further helping in applications like web services
discovery. SVM’s are useful for large scale data mining applications. Identifying the right
hyperplane that best separates the data points is the challenge here. Maximizing the distance
between nearest data point and hyperplane called as margin will help us to solve this issue.
Figure 6. SVM – Hyperplane separating classes
From Figure 6, it is clear that hyperplane C is high when compared with A and B
hyperplanes. This also makes the algorithm more robust to outliers. A linear classifier has the
form:
f(x) = W
T
X + b (6)
Where W is the normal to the line and b is the bias. W is also known as the weight vector. X
refers to the input vector in the above equation. Once the system is trained, the support vectors
are sufficient to classify the new test data unlike KNN method where we need to carry the
training data throughout classification.
Assuming that the data is perfectly separable, we need to optimize the following for
finding the support vectors. Minimize || w ||
2
, subject to:
(w · xi + b) ≥ 1, if yi = 1 (7)
(w · xi + b) ≤ −1, if yi = −1 (8)
The optimization algorithm that is required to generate the weight vectors will proceed in
such a way that the support vectors find the weights as well the boundary.
Though SVM works well with clear margin of separation and effective in high
dimensional spaces, it has its own limitations. These cons include performance degradation in
large data set due to higher training time and it does not perform well in case of noisy data set.
4. Experimental Results
For testing the different proposed algorithms along with the feature set selection, we
have used the OWL data set that is based on the Mooney Natural Language Learning Data. It
contains three different data sets including the geography, job and restaurant. We will pick up
few examples from the data set to explain the methodology:
a. How big is Texas – Geography domain
b. Are there any big jobs in Alameda? – Jobs
c. Give me some cafes in alameda – Restaurant
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Assuming that the above documents are used in training phase and let’s name them as
d1, d2 and d3 respectively. When we get a new test vector “t” as “Restaurants in Alameda”, we
observe the following outputs from different algorithms as described below:
1. K-NN Algorithm:
Find the document vectors first for all these documents. We then find the cosine
similarity between the test document and the available documents in the web as follows:
cos (t, d1) = 0
cos (t, d2) = 1/(√4 * √3) = 0.288
cos (t, d3) = 1/(√3 * √3) = 0.333
Cosine similarity is a quantity that is used to find the relationship between two vectors of
an inner product space that measures the cosine of the angle between the two vectors. The
cosine value of 0° is 1, and for other angles, it is less than one. Cosine similarity value needs to
be higher for more proximity. So in this case, the closest document is d3 for the test vector as
compared with the other documents. In case of a tie, we need to randomly choose the class and
it could be erroneous as well at times which pushes the need for us to test the next algorithm.
2. Centroid Classifier Algorithm:
Let us assume that there are two classes C1 and C2 and the document vectors for both
these classes are given as follows:
C1: (2,7), (3,8), (1,6)
C2: (6,4), (8,6), (5,2), (9,4)
We need to find the decision boundary between these two classes so that any new test
vector (e.g.: (12, 24)) can be classified based on that separating line. To do that, we first find the
centroids as per the algorithm:
µ(c1) = (((2+3+1)/3), ((7+8+6)/3)) = (2, 7) is the centroid of the first class.
µ(c2) = (((6+8+5+9)/4), ((4+6+2+4)/4)) = (7, 4) is the centroid of the second class.
Now we need to find the line that separates both the classes as shown in Figure 7. The
equation of a straight line is written in the form of y = mx + c where ‘m’ represents the slope of
the line and the place at which this line crosses the y-axis is the constant ‘c’ value. The slope in
this example is found as: (y2 – y1) / (x2 – x1) which is (7 – 4) / (2 – 7) giving the value as (-3/5).
So m1 equals to -3/5 while m2 equals to 5/3.
Figure 7. Centroid Classifier output
Thus y = (5/3) x + c. The constant value c is calculated as the midpoint or the mean of
the centroid values which is (4.5, 5.5). The decision boundary is thus derived in centroid
classifier algorithm. We also have the flexibility to adjust the centroids as and when new
document gets added to the repository or when legacy documents leave the database.
3. SVM Classifier Algorithm:
Let us assume that there are two classes C1 and C2 for support vector machines
classification with the following data set values:
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C1: {(0,0) (3,4) (5,9) (12, 1) (8,7) }
C2: {(9,8) (6,12) (10,8) (8,5) (14,8) }
As per the working principle of SVM, we first normalize the data followed by forming the
diagonal matrix and error matrix. We then find the augmented matrix as [A -E]. Since this is an
optimization problem, the Karush–Kuhn–Tucker (KKT) conditions are applied to find the
solution. The support vectors are finally calculated as W = A’DU and gamma value as –e’DU.
When a new test vector comes during the testing phase, we find the category of the test vector
through:
f(x) = sign (W' x - gamma) (9)
We can fine tune the results of this SVM classifier by modifying or adjusting the learning
constants. Though SVM is good in predicting, we observed that the time taken for training the
system is too high as compared to the rest of the algorithms discussed for this application.
Figure 8 represents the accuracy levels between the SVM and KNN algorithms where it is quite
clear that the SVM outperforms KNN.
Figure 8. SVM and KNN comparison
We have also studied the time taken to train the system for both these algorithms and
found that Centroid classifier algorithm would be much suitable when there are lots of updates
happening frequently and training has to happen or adjust accordingly. Fig 9 below represents
the training time across algorithms.
Figure 9. Centroid classifier and SVM training time comparison
We have also measured the precision and recall parameters across algorithms and
found that centroid classifier algorithm along with feature set selection excel when compared
with the other algorithms.
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5. Conclusion
The amount of data available in the internet is huge; especially text documents on
Internet, digital libraries etc. have seen a tremendous growth in the past few years. Automatic
data classification is hence an important task in web services discovery process. This
classification is challenging due to the larger attributes, huge training samples and attribute
needs. Data selection or reduction is always a major problem while working with huge data sets.
In this paper we have used three different algorithms for classification added with a new solution
for feature set selection. We find that with the proposed solution along with centroid classifier
algorithm gives better results as compared with the other methods. Moreover the computational
complexity of the proposed solution is much cheaper than the other methods in the literature.
Moving forward, we would like to explore further on fine tuning the different features in a
supervised learning algorithm. We would also like to extend the solution for mobile devices
which have even more constraints added to the complexity of wireless heterogeneous networks.
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