Asian Journal of Research in Computer Science

Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection.

This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity  and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection.

According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM   classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used. 

Looping is one of the fundamental logical instructions used for repeating a block of statements. All programming languages are used looping structures to simplify the programs. Loops are supported by all modern programming languages, though their implementations and syntax may differ. This paper compares the two types of looping structures while and do-while using the compile time and runtime of the given programs to improve the efficiency of the programs. It is found that for small number of iterations while is efficient in runtime and do-while is efficient in compile time, the difference in total execution time may not be considerable. But given any large number of iterations, the difference is noticeable.

Aims: The main aim of this paper is to propose a new boundary element method (BEM) algorithm for cancer modeling of cardiac anisotropy on the electrocardiogram (ECG) Simulation.

Study design:  Original research paper.

Place and Duration of Study: Jamoum laboratory, June 2018, Makkah, Saudi Arabia.

Methodology: a new boundary element algorithm was proposed and implemented for solving the governing equations of new cancer mathematical modeling in conjunction with the governing equations of ECG simulation.

Results: The effect of cardiac anisotropy on the ECG. Also, the effect of anisotropy on the relation between healthy and infected tissues.

Conclusion: For a known set of conductivities, numerical results show that the boundary element algorithm, for cancer modeling of cardiac anisotropy on the ECG simulation is very accurate, due to the excellent agreement of our results with the corresponding finite difference results, effects of anisotropic tissues that relate between people and (plants, insects and animals) are also studied as a new advantage for the proposed model.

Now-a-days cloud computing is a prominent way of providing resources and services in very secure manner. Gradually more and more organizations, companies and industries are picking up cloud technology for the safe keeping of their data. The objective of this work is to apply cloud service in healthcare system by building a practical patient health record (PHR) application and deploying it in the cloud. The system is ‘doctor-centric’ health record portal where only the doctor or hospital authority is responsible for securing their patients’ health data and this labor-free, paperless system is giving relief to the doctors and hospital authorities from various error-prone traditional health record keeping systems. Jelastic cloud is used to provide cloud service to the developed application which provides security, scalability, quality of service and ease of maintenance of the application. Jelastic cloud also provides load balancing whenever the user load is high. We are developing an interactive PHR application which is dynamically storing, creating, modifying and maintaining data and deploying it in the Jelastic CloudJiffy server by the use of InMotion Hosting server. CloudJiffy is India based fully redundant, high performance and scalable cloud “Platform-as-a-Service (PaaS)” under Jelastic Cloud Union. The whole system will be an efficient way for safe keeping Patients’ health records, their medical history and sensitive health information in a pervasive, confidential manner. The system is highly compatible for preserving medical records of eminent persons of our society and for those whose health information must be kept confidential in a highly secure way.

In educational data mining, the process of analysing and predicting from a pool of acquired data is a big challenge due to the influence of behavioural, environmental, parental, personal and social traits of students. While existing education predictive systems have used patterns generated from mined common factors to predict student performance based on subject, faculty, and grade amongst others, explicit traits, which defines a student are often neglected. Thus, such existing models are too general for specific and targeted analysis in more recent times when predictive features are although common but in real essence unique to individual students to a certain degree. Here, a Self-Academic Appraisal and Performance Predictive (SAAPP) system was developed to analyse and predict the overall performance of students before the expiration of their course duration. The inherent knowledge driven model analyses common available predictive internal and external factors, with probabilistic analysis of student academic history and pending courses. The system then builds a personal data centric system for individual student through a decision support expert system and a probabilistic optimal grade point analysis for more effective recommendation. The developed system is more accurate, reliable and precise in student performance classification with targeted recommendations.