Ayman E. Khedr

Enhancing Iterative Dichotomiser 3 algorithm for classification decision tree

Data mining tasks such as clustering and classification have proved to highly impact various fields such as business, including the banking sector, as well as medicine, including the radiology sector. As the decision‐making process is critically dependent on the availability of high‐quality information presented in a timely and easily understood manner, the successful application of efficient data mining approaches is a great support for achieving the required target in the available time. This study presents an enhancement for the Iterative Dichotomiser 3 (ID3) classification decision tree algorithm based on two related approaches, namely, data partitioning and parallelism. The study applied the proposed algorithm in the banking and radiology sectors; as data have been classified to the defined fields’ clusters, the processing time and the results’ accuracy parameters have been compared with the ID3 algorithm and have proved an enhancement in both parameters. WIREs Data Mining Knowl Discov 2016, 6:70–79. doi: 10.1002/widm.1177

Cross Language Information Retrieval Model for Discovering WSDL Documents Using Arabic Language Query

Web service discovery is the process of finding a suitable Web service for a given user’s query through analyzing the web service‘s WSDL content and finding the best match for the user’s query. The service query should be written in the same language of the WSDL, for example English. Cross Language Information Retrieval techniques does not exist in the web service discovery process. The absence of CLIR methods limits the search language to the English language keywords only, which raises the following question “How do people that do not know the English Language find a web service, This paper proposes the application of CLIR techniques and IR methods to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on WSDL content and user’s query to be ready for CLIR methods. The proposed model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and used for solving the research problem with 99.87 % accuracy and 95.06 precision

A Proposed Integrated Approach for BI and GIS in Health Sector to Support Decision Makers (BIGIS-DSS)

This paper explores the possibilities of adopting Business Intelligence (BI), and Geographic Information System (GIS) to build a spatial intelligence and predictive analytical approach. The proposed approach will help in solving spatial problem which faces decision makers at health sector. The proposed spatial analytical approach will cover three main health planning issues. These issues are tackling health inequalities through geospatial monitor for inequalities in distribution of health units and its services, support decision-making with predictive analytics for common health indicators, and geoprocessing for input layers through dynamic health map and motion charts to support decision making.

An integration framework for search engine architecture to improve information retrieval quality

This Study presents a smart information retrieval methodology/smart retrieval query technique that depends on the power of search engine, clawers, full text indexing, and descriptions points for documents contents or websites as known as “An integration framework for search engine architecture to improve information retrieval quality” or smart information retrieval. The new idea for search engine architecture able to make search statement or document print that used in searching operations which depend on Boolean retrieval that uses Boolean algebra and truth table comparative technique. Search engine indexer makes indexing for documents and web sites contents which depend on the performance and quality of search engine, indexer and web clawer to produce precision, recall through crawling and indexing operations to identify folding and stemming words according to smart web query engine which has accurate crawler architecture, truth table comparative technique and search statement or document print.

Intelligent Decision Framework to Explore and Control Infection of Hepatitis C Virus

This research presents Intelligent Decision Framework (IDF) to explore and manage cases of hepatitis c virus based on data mining approach and Fuzzy logic system. The proposed framework is produced from integration between data mining decision tree, rule based classification and fuzzy logic system. On the other hand, this study improves the predication results of Fibrosis stage by using Trapezoidal Fuzzy Number (TFN) distribution as fuzzy logical system to arrive 98.1% compared to predication results that were 92.5% by data mining decision tree model for same patients sample. Fuzzy logic system predicts disease scale of Hepatitis C Virus (HCV) for patients sample through different stages of liver disease caused by virus c. The proposed framework supports physicians and Ministry of Health (MOH) strategies for treatment to limit and control HCV infections and prevalence rate in Egypt and other countries. The extracted knowledge and information from proposed framework helps decision makers to take appropriate and better decision at appropriate time to against hepatitis c viral in world. The architecture of intelligent decision framework is designed to support physicians to investigate and present treatment for HCV cases. Also, to develop intelligent machine, health care system or robots as a physician for HCV patients in high prevalence rate countries.

A proposed configurable approach for recommendation systems via data mining techniques

ABSTRACT This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

Adaptive Classification Method Based on Data Decomposition

Knowledge discovery is one of the vital fields which strongly supports decision making by applying different techniques based on the targeted field and the required information. Focusing on clustering and classification techniques, this paper presents an approach for adapting one of the classification algorithms for supporting decision making procedure in radiology data analysis field. The proposed adaptation is based on dividing the analysis problem by data partitioning and individually examining against each cluster, with applying the classification algorithm in a parallel approach. The proposed approach has proved to produce higher results accuracy with minimization of time when compared with the traditional ID3.

Cross-Language Semantic Web Service Discovery to Improve the Selection Mechanism by using Data Mining Techniques

Web service became one of the important methods for communication through the internet and its usage increased in the levels of users and developers. Semantic web service represents the second generation of web services that contains more description and information about its contents. Searching and dealing with web service is done through process called web service discovery which returns a Semantic Web Service Description Language file (SWSDL) for each web service. This research aims to expand the semantic web service usage through adding the multilanguage capability to the web service’s discovery process and through recommending other web services to the user based on his history in using web services. These aims were achieved by modifying the web service discovery model through adding two important techniques the Cross Language Information Retrieval (CLIR) technique and the data mining association rules technique. This research proposed two sub models, the first sub model proposed the application of CLIR techniques and information retrieval method to support Bilingual Web service discovery process the second language that proposed here is Arabic. Text mining techniques were applied on SWSDL content and user’s query to be ready for CLIR methods, this sub model was tested on a curated catalogue of Life Science Web Services http://www.biocatalogue.org/ and achieving 99.38 % accuracy and 87.23 precision of the effectiveness of the monolingual system. The second sub model proposed a process of web service recommendation by applying the data mining techniques to suggest another web service beside the one he got from the discovery process based on the user’s history. This sub model was tested on the mention curated web services site and the results were 65 % of users chose services from the services that recommended by the proposed sub model.

Semantic Conflicts Reconciliation as a Viable Solution for Semantic Heterogeneity Problems

t—Achieving semantic interoperability is a current challenge in the field of data integration in order to bridge semantic conflicts occurring when the participating sources and receivers use different or implicit data assumptions. Providing a framework that automatically detects and resolves semantic conflicts is considered as a daunting task for many reasons, it should preserve the local autonomy of the integrated sources, as well as provides a standard query language for accessing the integrated data on a global basis. Many existing traditional and ontology-based approaches have tried to achieve semantic interoperability, but they have certain drawbacks that make them inappropriate for integrating data from a large number of participating sources. We propose semantic conflicts reconciliation (SCR) framework, it is ontology-based system in which all data semantics explicitly described in the knowledge representation phase and automatically taken into account through the interpretation mediation service phase, so conflicts detected and resolved automatically at the query time.

A Proposed Defect Tracking Model for Classifying the Inserted Defect Reports to Enhance Software Quality Control

CONFLICT OF INTEREST: NONE DECLARED Defect tracking systems play an important role in the software development organizations as they can store historical information about defects. There are many research in defect tracking models and systems to enhance their capabilities to be more specifically tracking, and were adopted with new technology. Furthermore, there are different studies in classifying bugs in a step by step method to have clear perception and applicable method in detecting such bugs. This paper shows a new proposed defect tracking model for the purpose of classifying the inserted defects reports in a step by step method for more enhancement of the software quality.