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| تعداد مقالات | 9,644 |
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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 | ||
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آمار تعداد مشاهده مقاله: 716 تعداد دریافت فایل اصل مقاله: 417 |
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