Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78535120160601Specification-based Test Cases Generation for Multi-Level Service Composition173395010.21608/ijci.2016.33950ENShaymaaAbdelaalFaculty of Computers and Information,
Menofia University, EgyptMahmoudHussienFaculty of Computers and Information,
Menofia University, Egypt0000-0002-3742-7548AshrafElsisiFaculty of Computers and Information,
Menofia University, EgyptJournal Article20160105Testing is the traditional validation method in the software industry. To ensure the delivery of high quality and<br />robust service-oriented applications, testing of web services composition has received much attention. These services have become more and more complex, where they have to cope with strict requirements of business processes and their<br />dynamic evolution, and interactions among different companies. In this context, the analysis and testing of such services<br />demand a large amount of effort. To reduce the effort required for web-services testing, in this paper, we propose a<br />specification-based approach to automatically generate test cases for web services composition that is modeled at different levels of abstraction. This approach specifies a service structure as multi-level models. To generate the test cases, it checks if the first level of the model has a parallel execution or a decision table to be solved by an algorithm that solves Chinese postman problem. Then, it identifies paths for last level of the model and relates the results of all levels with each other. To evaluate our approach, we applied it to four cases study using our developed tool. Compared to exiting approaches, our approach reduces testing cost and execution time, and increases testing reliability.<br /><br />Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78535120160601DTSRS: A Dynamic Trusted Set based Reputation System for Mobile Participatory Sensing Applications8233395310.21608/ijci.2016.33953ENHayamMousaFaculty of Computers and Information, Menoufia University, EgyptMohyHadhoudFaculty of Computer and Information Menoufia UniversityOsamaYounesFaculty of Computers and Information, Menoufia University, EgyptSoniaBen MokhtarLIRIS, INSA de Lyon, FranceLionelBrunieLIRIS, INSA de Lyon, FranceJournal Article20160105Participatory sensing is an emerging paradigm in which citizens voluntarily use their mobile phones to capture and share sensed data from their surrounding environment in order to monitor and analyze some phenomena (e.g., weather, road traffic, pollution, etc.). Participating users can disrupt the system by contributing corrupted, fabricated, or erroneous data. Different reputation systems have been proposed to monitor participants' behavior and to estimate their honesty. There are some attacks that were not considered by the existing reputation systems in the context of participatory sensing applications including corruption, collusion, and on-off attack. In this paper, we propose a more robust and efficient reputation system designed for these applications. Our reputation system incorporates a mechanism to defend against those attacks. Experimental results indicate that our system can accurately estimate the quality of contributions even if collusion is committed. It can tolerate up to 60% of colluding adversaries involved in the sensing campaign. This enables our system to aggregate the data more accurately compared with the state-of-the art. Moreover, the system can detect adversaries even if they launch on-off attack and strategically contribute some good data with high probability (e.g. 0.8).<br /><br />Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78535120160601A Comparative Study for Arabic Text Classification Based on BOW and Mixed Words Representations24343395410.21608/ijci.2016.33954ENRouhia M.SallamFaculty of Applied Sciences, Taiz University, YemenHamdyMousaFaculty of Computers and Information Menoufia University0000-0001-9503-9124MahmoudHussienFaculty of Computers and Information,
Menofia University, Egypt0000-0002-3742-7548Journal Article20160105This paper compares two methods for features representation in Arabic text classification. These methods are bag of words (BOW) that mean the word-level unigram and mixed words representations. The mixed words use a mixture of a bag of words and two adjacent words with different proportions. The main objective of this paper is to measure the accuracy of each method and to determine which method is more accurate for Arabic text classification based on the representation modes. Each method uses normalization and stemming. The results show that the use of mixed words in features representation achieves the highest accuracy by 98.61% when normalization is used.<br /><br />Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78535120160601Semantic-based Approach for Solving the Heterogeneity of Clinical Data35453395510.21608/ijci.2016.33955ENBasmaElsharkawyFaculty of Computers and Information,
Menoufia University, Shebin Elkom, Egypt.HatemAhmedFaculty of Computer and Information Menoufia UniversityRashedSalemFaculty of Computers and Information,
Menoufia University, Shebin Elkom, Egypt.Journal Article20160105Clinical records contain massive heterogeneity number of data types, generally written in free-note without a linguistic standard. Other forms of medical data include medical images with/without metadata (e.g., CT, MRI, radiology, etc.), audios (e.g., transcriptions, ultrasound), videos (e.g., surgery recording), and structured data (e.g., laboratory test<br />results, age, year, weight, billing, etc.). Consequently, to retrieve the knowledge from these data is not trivial task.<br />Handling the heterogeneity besides largeness and complexity of these data is a challenge. The main purpose of this paper<br />is proposing a framework with two-fold. Firstly, it achieves a semantic-based integration approach, which resolves the<br />heterogeneity issue during the integration process of healthcare data from various data sources. Secondly, it achieves a<br />semantic-based medical retrieval approach with enhanced precision. Our experimental study on medical datasets<br />demonstrates the significant accuracy and speedup of the proposed framework over existing approaches.<br /><br />