النموذج المبدئي ل MyBotS على وسائل التواصل الاجتماعي Discord مع البرمجة اللغوية العصبية
محتوى المقالة الرئيسي
الملخص
أدى النمو المستمر في التكنولوجيا والأجهزة التكنولوجية إلى تطوير الآلات للمساعدة في تسهيل الأنشطة المختلفة المتعلقة بالبشر. على سبيل المثال ، بغض النظر عن أهمية المعلومات على منصة Steam ، لا يزال المشترون أو اللاعبون يحصلون على القليل من المعلومات المتعلقة بالتطبيق. هذا غير مشجع على الرغم من أهمية المعلومات في عصر العولمة الحالي. لذلك ، من الضروري تطوير تطبيق جذاب وتفاعلي يسمح للمستخدمين بطرح الأسئلة والحصول على إجابات ، مثل chatbot ، والذي يمكن تنفيذه على وسائل التواصل الاجتماعي Discord. الذكاء الاصطناعي هو تقنية تسمح للآلات بالتفكير والقدرة على اتخاذ قراراتها الخاصة. أظهر هذا البحث أن نموذج chatbot الخاص بـ discord يوفر خدمات متنوعة بناءً على نتائج اختبار التصنيف باستخدام طريقة SVM بثلاث نوى ، وهي Linear و Polynomial و RBF. تعد بيانات الاختبار وتنبؤ قيم الدقة أكبر Liniear Kernel SVM بدقة وقيم توقع خطأ تبلغ 94٪ و 6٪.
Received 21/12/2020
Accepted 15/3/2021
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
المراجع
Raihan JP, Putri YR. Communication Pattern Discord Group PUBG.INDO.FUN Through Application Discord. e-Proceeding Manag. 2018;2(3):4161–9.
Lacher L, Biehl C. Using Discord to Understand and Moderate Collaboration and Teamwork. Proc 49th ACM Tech Symp Comput Sci Educ. 2018;1107–1107.
Wulanjani AN. Discord Application:Turning a Voice Chat Application for Gamers into a Virtual Listening Class. 2nd English Lang Lit Int Conf. 2018;2:115–9.
Martínez-Plumed F, Loe BS, Flach P, Eéigeartaigh S, Vold K, Hernández-Orallo J. The facets of artificial intelligence: A framework to track the evolution of AI. IJCAI Int Jt Conf Artif Intell. 2018;2018-July:5180–7.
Juhn Y, Liu H. Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. J Allergy Clin Immunol [Internet]. 2020;145(2):463–9. Available from: https://doi.org/10.1016/j.jaci.2019.12.897
Zahour O, Benlahmar EH, Eddaoui A, Ouchra H, Hourrane O. A system for educational and vocational guidance in Morocco: Chatbot e-orientation. Procedia Comput Sci [Internet]. 2020;175:554–9. Available from: https://doi.org/10.1016/j.procs.2020.07.079
Septiansyah R, Akbar SR, Maulana R. Perancangan Bot Pada Discord Untuk Pembacaan Sensor Di Raspberry Pi Dengan Sistem Learning Yang Dinamis. J Pengemb Teknol Inf dan Ilmu Komput Univ Brawijaya. 2018;2(10):4202–12.
Niell S, Jesús F, Díaz R, Mendoza Y, Notte G, Santos E, et al. Beehives biomonitor pesticides in agroecosystems: Simple chemical and biological indicators evaluation using Support Vector Machines (SVM). Ecol Indic [Internet]. 2018;91(May 2017):149–54. Available from: https://doi.org/10.1016/j.ecolind.2018.03.028
Zhang H. Analysis of artificial Intelligence Technology in Electric Automation Control. J Phys Conf Ser. 2019;1345(5).
Yang LB. Application of artificial intelligence in electrical automation control. Procedia Comput Sci [Internet]. 2020;166:292–5. Available from: https://doi.org/10.1016/j.procs.2020.02.097
Heller B, Procter M, Mah D. Freudbot: An investigation of chatbot technology in distance education. Proc World Conf Educ Multimedia, Hypermedia Telecommun [Internet]. 2005;(March 2016):3913–8. Available from: http://www.editlib.org/index.cfm?fuseaction=Reader.ViewFullText&paper_id=20691
Beaudry J, Consigli A, Clark C, Robinson KJ. Getting ready for adult healthcare: Designing a chatbot to coach adolescents with special health needs through the transitions of care. J Pediatr Nurs [Internet]. 2019;49:85–91. Available from: https://doi.org/10.1016/j.pedn.2019.09.004
Sutoyo R, Chowanda A, Kurniati A, Wongso R. Designing an emotionally realistic chatbot framework to enhance its believability with AIML and information states. Procedia Comput Sci [Internet]. 2019;157:621–8. Available from: https://doi.org/10.1016/j.procs.2019.08.226
Dhyani M, Kumar R. An intelligent Chatbot using deep learning with Bidirectional RNN and attention model. Mater Today Proc [Internet]. 2020;(xxxx). Available from: https://doi.org/10.1016/j.matpr.2020.05.450
Setiaji B, Wibowo FW. Chatbot Using a Knowledge in Database: Human-to-Machine Conversation Modeling. Proc - Int Conf Intell Syst Model Simulation, ISMS. 2016;0:72–7.
Jain A, Kulkarni G, Shah V. Natural Language Processing. Int J Comput Sci Eng. 2018;6(1):161–7.
Garousi V, Bauer S, Felderer M. NLP-assisted software testing: A systematic mapping of the literature. Inf Softw Technol. 2020;126(March).
Bruno Marietto M das G, Aguiar RV, Barbosa G de O, Botelho WT, Pimentel E, Franca R dos S, et al. Artificial Intelligence Markup Language: A Brief Tutorial. Int J Comput Sci Eng Surv. 2013;4(3):1–20.
Abdul-Kader SA, Woods JC. Survey on Chatbot Design Techniques in Speech Conversation Systems. Int J Adv Comput Sci Appl. 2015;6(7):72–80.
Miner G, Elder J, Hill T, Delen D. A Fast, Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications. Acad Press Waltham, MA. 2012;
Mooney RJ. Machine Learning Text Categorization. Mach Learn Text Categ. 2006;1–6.
Engelhart MD, Aiken LR. The text mining handbook: Advanced approaches in analyzing unstructured data. Educ Psychol Meas. 1975;35(1):199–199.
Cummins R, O’Riordan C. Evolved term-weighting schemes in information retrieval: An analysis of the solution space. Artif Intell Rev. 2006;26(1–2):35–47.
Soucy P, Mineau GW. Beyond TFIDF weighting for text categorization in the vector space model. IJCAI Int Jt Conf Artif Intell. 2005;1130–5.
Cummins R. The Evolution and Analysis of Term-Weighting Schemes in Information Retrieval. Analysis. 2008;201.
Kumari M, Jain A, Bhatia A. Synonyms Based Term Weighting Scheme: An Extension to TF.IDF. Procedia Comput Sci [Internet]. 2016;89:555–61. Available from: http://dx.doi.org/10.1016/j.procs.2016.06.093
Salton G, Buckley C. TERM-WEIGHTING APPROACHES IN AUTOMATIC TEXT RETRIEVAL GERARD. Inf Process Manag. 1988;24(5):513–23.
Munot N, Govilkar SS. Comparative Study of Text Summarization Methods. Int J Comput Appl. 2014;102(12):33–7.
Christian H, Agus MP, Suhartono D. Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF). ComTech Comput Math Eng Appl. 2016;7(4):285.
Susilowati E, Sabariah MK, Gozali AA. Implementation Support Vector Machine Method for Traffic Jam Classification on Twitter. e-Proceeding Eng. 2015;2(1):1478–84.
Demidova LA, Klyueva IA, Pylkin AN. Hybrid approach to improving the results of the SVM classification using the random forest algorithm. Procedia Comput Sci [Internet]. 2019;150:455–61. Available from: https://doi.org/10.1016/j.procs.2019.02.077
Srivastava DK, Bhambhu L. Data classification using support vector machine. J Theor Appl Inf Technol. 2010;12(1):1–7.