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تشخیص بیماریهای نیوکاسل، برونشیت و آنفلوانزای پرنده با استفاده از سیگنال صدای قلب و ماشین بردار پشتیبان | ||
مهندسی بیوسیستم ایران | ||
مقاله 1، دوره 47، شماره 4، بهمن 1395، صفحه 587-601 اصل مقاله (998.26 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2017.60253 | ||
نویسندگان | ||
محمد صادقی1؛ احمد بناکار* 1؛ عبدالحمید شوشتری2 | ||
1دانشگاه تربیت مدرس | ||
2موسسه تحقیقات واکسن و سرم سازی رازی | ||
چکیده | ||
در این پژوهش روشی هوشمند به منظور تشخیص توامان بیماریهای نیوکاسل، آنفلوانزا و برRBFونشیت پرنده از روی سیگنال صدای قلب پرداخته شده است. در ابتدا جوجهها به 4 دسته تقسیم شدند. یک گروه به عنوان نمونههای شاهد در نظر گرفته شدند و با ویروس هیچگونه تماسی نداشتند و 3 گروه باقیمانده به ترتیب به ویروسهای نیوکاسل، آنفلوانزا و برونشیت آلوده شدند. سیگنالهای حوزه زمان صدای قلب توسط تبدیل فوریه سریع و تبدیل موجک گسسته دابچی نوع اول در دو سطح تجزیه به ترتیب به حوزههای فرکانس و زمان- فرکانس انتقال داده شدند. در مرحله دادهکاوی از سیگنالهای هر سه حوزه 25 ویژگی آماری استخراج شدند و با استفاده از IDE بهترین ویژگیها انتخاب شدند. با استفاده از ماشین بردار پشتیبان و نظریه شواهد دمپستر- شافر سیگنالهای صدای قلب جوجهها طبقهبندی شدند. دقت میانگین، Specificityو Sensitivity تلفیق طبقهبندها به منظور تشخیص بیماریها به ترتیب93/81، 29/93 و 28/82 درصد به دست آمد. | ||
کلیدواژهها | ||
ماشین بردار پشتیبان؛ تبدیل موجک گسسته؛ بیماری طیور؛ نظریه شواهد دمپستر – شافر | ||
عنوان مقاله [English] | ||
Diagnosing avian Newcastle, Bronchitis and Influenza Diseases using heart sound signal and Support Vector Machine | ||
چکیده [English] | ||
This study represents an intelligence procedure for diagnosis simultaneously avian Newcastle Disease Virus, Infection Bronchitis Virus and Influenza using heart sound signal. For this aim, the chickens were divided into four groups. The first group was considered as control samples. The second, third and fourth groups were infected with Newcastle Disease Virus, Infection Bronchitis and Avian Influenza, respectively. The time domain signals were transferred to the frequency and time-frequency domain using Fast Fourier Transform and Discrete Wavelet Transform. In data mining stage, 25 statistical features were extracted from three domains and the best features were selected using improved distance evaluation (IDE) method. The heart sound signals were classified using multiclass support vector machine and Dempster-Shafer evidence theory. The total accuracy, Specificity and Sensitivity of classifiers fusion in diagnosing avian diseases were obtained 81.93, 93.29 and 82.28 percent respectively. | ||
کلیدواژهها [English] | ||
Support Vector Machine (SVM), Discrete wavelet transform (DWT), Avian Disease, Dempster-Shafer evidence theory | ||
مراجع | ||
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