پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 708 |
| تعداد مقالات | 10,180 |
| تعداد مشاهده مقاله | 71,589,252 |
| تعداد دریافت فایل اصل مقاله | 63,362,282 |
ارائه یک سیستم خبره به کمک الگوریتم فراابتکاری حسابی برای تشخیص بیماری عفونیکووید-19 | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 73، تیر 1402، صفحه 47-61 اصل مقاله (1.18 M) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.27986.2312 | ||
| نویسندگان | ||
| فائزه ابراهیمی* 1؛ علیرضا آذری مقدم2؛ محسن نژادخیرالله3 | ||
| 1دانشگاه علامه محدث نوری -نور -گروه کامپیوتر | ||
| 2موسسه آموزش عالی مازیار-نور-گروه کامپیوتر | ||
| 3دانشگاه علامه محدث نوری-نور-گروه کامپیوتر | ||
| تاریخ دریافت: 0-313 فروردین 781، تاریخ بازنگری: 0-197 فروردین 781، تاریخ پذیرش: 0-153 فروردین 781 | ||
| چکیده | ||
| دو سال گذشته حیاتیترین و بحرانیترین دوره همهگیر بیماری کووید-19 بوده است. این بیماری بر بیشتر جنبههای زندگی در سراسر جهان تأثیر گذاشته است. از دیدگاه بالینی، چندین روش برای تشخیص زودهنگام بیماری موجود میباشد، اما قابلیت این روشها محدود بوده است. در نتیجه، مطالعات بسیاری برای تشخیص خودکار بیماری صورت گرفته است. هوشمصنوعی راهحلهای فنی بالقوهای را دراختیار جامعه پزشکی قرار داده تا بر اساس علائم بالینی تشخیص سریع صورت گیرد. دراین پژوهش باکمک یک پرسشنامه علائم بالینی (تب، سرفه، گلودرد، تنگی نفس، ضعف، حسچشایی وبویایی، محیط) بیمار بررسی شده، سپس دادههای ورودی به یکی ازروشهای سیستم مبتنی برقانون یا سیستم فازی مورد بررسی قرارمیگیرد، درروش فازی باکمک الگوریتم فراابتکاری حسابی پارامتر وزن بهینه گردیده سپس منطق فازی با وزن بهینه شده برای دادهها بکارگرفته میشود. درپایان خروجی به یکی از صورتهای فردسالم، کروناخفیف، کرونا متوسط، کرونا شدید نشان داده میشود. این مقاله برروی چندین پایگاه داده که بترتیب حدود600داده ازطریق پرسشنامه توسط افراد تکمیل شده، 2000 داده که سازمان جهانی بهداشت برای بیماران کرونایی منتشرکرده وحدود 400داده که ازطریق دادههای بیمارستانی فراهم گردیده است. ملاحظه گردیددر این سیستم میزان دقت برابربا 98%، حساسیت100%، ویژگی 98% و نمره اهمیت 95% میباشد. | ||
| کلیدواژهها | ||
| تشخیص؛ بیماریعفونی کووید-19؛ منطق فازی سوگنو؛ الگوریتم فراابتکاری حسابی؛ مبتنی برقانون روبه جلو | ||
| عنوان مقاله [English] | ||
| Providing an expert system with the help of Arithmetic meta-heuristic algorithm to diagnose the infectious disease of covid-19 | ||
| نویسندگان [English] | ||
| Faezeh Ebrahimi1؛ Alireza Azari Moghaddam2؛ Mohsen Nejad Khairullah3 | ||
| 1Allameh Mohaddes Nouri University-Nour-Computer group. | ||
| 2Maziar Higher Education Institute-Nour-Computer Faculty | ||
| 3Allameh Mohaddes Nouri University-nour-Computer Faculty | ||
| چکیده [English] | ||
| The last two years have been the most critical and critical period of the Covid-19 pandemic.This disease has affected most aspects of life around the world. From a clinical point of view, several methods are available for early diagnosis of the disease, but the capabilities of these methods have been limited. As a result, many studies have been conducted to automatically diagnose the disease. Artificial intelligence has provided potential technical solutions to the medical community to make quick diagnosis based on clinical symptoms.In this research, the patient's clinical symptoms (fever, cough, sore throat, shortness of breath, weakness, sense of taste and smell, environment) are examined with the help of a questionnaire,then the input data is examined by one of rule based system or fuzzy system methods, in the fuzzy method with the help of arithmetic meta-heuristic algorithm. The optimized weight parameter is then applied to the data with the optimized weight fuzzy logic. At the end, the output will be shown as healthy person, weak corona, middel corona, high corona. This article is based on several databases, in which about 600 data have been completed by individuals through questionnaires, 2000 data have been published by the World Health Organization for corona patients, and about 400 data have been provided through hospital data. It was observed that in this system, the accuracy rate is equal to 98%, sensitivity is 100%, specificity is 98% and the F- score is 95%. | ||
| کلیدواژهها [English] | ||
| Diagnosis, infectious disease covid-19, Sogno', s fuzzy logic, Arithmetic meta-heuristic algorithm, Forward chaining rule based | ||
| مراجع | ||
|
[1] C. Calabrese, E. Kirchner, and L. H. Calabrese, "Long COVID and rheumatology: Clinical, diagnostic, and therapeutic implications," Best Practice & Research Clinical Rheumatology, p. 101794, 2022. [2] R. Islam, E. Abdel-Raheem, and M. Tarique, "A study of using cough sounds and deep neural networks for the early detection of COVID-19," Biomedical Engineering Advances, vol. 3, p. 100025, 2022. [3] T. A. Soomro, L. Zheng, A. J. Afifi, A. Ali, M. Yin, and J. Gao, "Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): A detailed review with direction for future research," Artificial Intelligence Review, vol. 55, no. 2, pp. 1409-1439, 2022. [4] B. Alsaaidah, M. d. R. Al-Hadidi, H. Al-Nsour, R. Masadeh, and N. AlZubi, "Comprehensive Survey of Machine Learning Systems for COVID-19 Detection," Journal of Imaging, vol. 8, no. 10, p. 267, 2022. [5] M. Ahmed, A. Rahman, M. Farooqui, F. Alamoudi, R. Baageel, and A. Alqarni, "Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System," Mathematical Modelling of Engineering Problems, pp. 805-812, 2021. [6] Z. Kou, L. Shang, Y. Zhang, Z. Yue, H. Zeng, and D. Wang, "Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection," in Proc. Int. Joint Conf. Artif. Intell.(IJCAI), 2022, pp. 5087-5093. [7] V. V. Khanna, K. Chadaga, N. Sampathila, S. Prabhu, R. Chadaga, and S. Umakanth, "Diagnosing COVID-19 using artificial intelligence: a comprehensive review," Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 11, no. 1, pp. 1-23, 2022. [8] Y. Peng, E. Liu, S. Peng, Q. Chen, D. Li, and D. Lian, "Using artificial intelligence technology to fight COVID-19: a review," Artificial Intelligence Review, pp. 1-37, 2022. [9] B. González-Pérez, C. Núñez, J. L. Sánchez, G. Valverde, and J. M. Velasco, "Expert System to Model and Forecast Time Series of Epidemiological Counts with Applications to COVID-19," Mathematics, vol. 9, no. 13, p. 1485, 2021. [10] H. Şimşek and E. Yangın, "An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic," Health and Technology, pp. 1-14, 2022. [11] M. Shatnawi, A. Shatnawi, Z. AlShara, and G. Husari, "Symptoms-based Fuzzy-Logic Approach for COVID-19 Diagnosis," International Journal of Advanced Computer Science And Application (IJACSA), vol. 12, no. 4, 2021. [12] G. Wu et al., "Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study," European Respiratory Journal, vol. 56, no. 2, 2020. [13] C. Jin et al.,"Development and evaluation of an artificial intelligence system forCOVID-19 diagnosis," Nature communications, vol. 11, no. 1, 2020, pp. 1-14. [14] X. Xu et al.,"A deep learning system to screen novel coronavirus disease pneumonia," Engineering, vol. 6, no. 10, 2020, pp. 1122-1129 [15] K. Shankar and E. Perumal, "A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images," Complex & Intelligent Systems, vol. 7, no. 3, pp. 1277-1293, 2021. [16] M. Dehghandar, M. Pabasteh, and R. Heydari, "Diagnosis of COVID-19 disease by fuzzy expert system designed based on input-output," Journal of Control, vol. 14, no. 5, pp. 71-78, 2021. [17] K. F. Haque, F. F. Haque, L. Gandy, and A. Abdelgawad, "Automatic detection of COVID-19 from chest X-ray images with convolutional neural networks," in 2020 international conference on computing, electronics & communications engineering (iCCECE), 2021, pp. 125-130: IEEE. [18] A. A. S. Asl, M. M. Ershadi, and S. Sotudian, "Fuzzy Expert Systems for Prediction of ICU Admission in Patients with COVID-19," arXiv preprint arXiv:2104.12868, 2022. [19] Q. Hu et al., "Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification," Applied Soft Computing, vol. 123, p. 108966, 2022. [20] L. V. De Moura, C. Mattjie, C. M. Dartora, R. C. Barros, and A. M. M. da Silva, "Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography," Frontiers in digital health, vol. 3, 2022. [21] M. Jayalakshmi et al., "Fuzzy logic-based health monitoring system for covid'19 patients," Cmc-Computers Materials & Continua, pp. 2430-2446, 2021. [22] S. Asadi et al., "Evaluation of factors to respond to the COVID-19 pandemic using DEMATEL and fuzzy rule-based techniques," International Journal of Fuzzy Systems, vol. 24, no. 1, pp. 27-43, 2022 [23] M. Kamarzarrin , "Modeling of self-assessment system of COVID-19 disease diagnosis using Type-2 Sugeno fuzzy inference system," Journal of Control, vol. 14, no. 5, pp. 49-57, 2021. [24] L. Abualigah, A. Diabat, S. Mirjalili, M. Abd Elaziz, and A. H. Gandomi, "The arithmetic optimization algorithm," Computer methods in applied mechanics and engineering, vol. 376, p. 113609, 2021. [25] A. Arian et al., "Evaluation of chest CT-scan appearances of COVID-19 according to RSNA classification system," Journal of Family Medicine and Primary Care, vol. 11, no. 8, pp. 4410-4416, 2022. | ||
|
آمار تعداد مشاهده مقاله: 906 تعداد دریافت فایل اصل مقاله: 575 |
||