بهینه سازی چندگانه برای ارزیابی الکترونیکی
ترجمه نشده

بهینه سازی چندگانه برای ارزیابی الکترونیکی

عنوان فارسی مقاله: ماهیت شناسی خودکار مبتنی بر بهینه سازی ازدحام چندگانه برای ارزیابی الکترونیکی
عنوان انگلیسی مقاله: Multi Swarm Optimization based Automatic Ontology for E-Assessment
مجله/کنفرانس: شبکه های کامپیوتری – Computer Networks
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: ماهیت شناسی خودکار، ارزیابی الکترونیکی، الگوریتم کاهش سریع بدون نظارت، بهینه سازی ازدحام چندگانه، الگوی آماری
کلمات کلیدی انگلیسی: automated ontology, e-assessment, unsupervised quick reduct algorithm, multi-swarm optimization, statistical pattern
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.comnet.2019.03.011
دانشگاه: Department of Computer Science and Engineering, National Institute of Technology, Trichy 620015, TamilNadu, India
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.205 در سال 2018
شاخص H_index: 119 در سال 2019
شاخص SJR: 0.592 در سال 2018
شناسه ISSN: 1389-1286
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 17
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13679
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1. Introduction


2. Literature survey


3. Proposed automated ontology method


4. Experimental results


5. Conclusion


Conflict of interest


Appendix. Supplementary materials


Research Data


References

نمونه متن انگلیسی مقاله

Abstract


The utilization of ontology in the e-assessment area has grown tremendously. The context of e-learning is significant to the students for educational purposes. This makes the testing process easy for the students and also for the teachers. The majority of the approaches that deals with the ontology issue have suggested that the individual ontology models have merely a fraction of the assessment domain. To trounce such drawbacks, here, an automated ontology creation is proposed for the e-assessment systems. Initially, the text is extracted from the web utilizing the Unsupervised Quick Reduct (UQR) algorithm. This is trailed by the summarization of the texts using the multi-swarm optimization (MSO) based on preference learning. Finally, the sentence of the summary is then transmuted to multiple choice questions (MCQ). The keys are created using statistical pattern (SP). The efficiency of the system is examined using the experimental outcomes like error rate, precision, recall and accuracy. In accuracy, the proposed UQR algorithm achieves 97.7%, MSO achieve 96.2% accuracy and key generation achieves 94.7% accuracy. The proposed automatic ontology system indicates better when weighed against the top-notch methods.


Introduction


In the recent age, learning took a new trend owing to the evolving technology. E-Learning is basically a web-based communication platform that allows a student to learn irrespective of the geographic distance and time. There is access also to diverse learning tools say discussion boards, assessments and content repositories [1-5]. Context e-learning provides students with a platform for improving their knowledge. Improving the learning capacity and managing the evaluation process by themselves are the goals of ideal students [6]. The traditional barriers in education are totally broken by the commencement of the e-learning system. The testing and valuation phase is not a manual process anymore but it is completely automated. Automated ontology has appeared as an interesting research area in the field of e-learning and assessment [7-9]. Assessment is basically a procedure for discussing the information and evaluating the knowledge of students. Along with these, interesting questions are automatically generated for a different domain. These questions are taken from web documents, journals, research papers and articles that are mentioned by the users. The automatic ontology aimed at eassessment can well be implemented using fuzzy systems [10], neural network [11] and other optimization techniques [12-14]. In general ontology, there are three components such as sentence with blank, key and the distracters. Ontologies are being widely used in information retrieval, question answering and decision support systems.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

بهینه سازی چندگانه برای ارزیابی الکترونیکی
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها