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تشخیص اردک های بیمار بر اساس صدای آنها و به کمک روش هوش مصنوعی | ||
مهندسی بیوسیستم ایران | ||
مقاله 12، دوره 47، شماره 2، شهریور 1395، صفحه 307-318 اصل مقاله (829.46 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/ijbse.2016.58780 | ||
نویسندگان | ||
احمد بناکار* 1؛ مفداد خزایی2 | ||
1استادیار دانشگاه تربیت مدرس | ||
2دانشجوی دکتری | ||
چکیده | ||
در این مقاله روشی هوشمند به منظور طبقهبندی اردکهای سالم و بیمار بر اساس صدای منتشره از آنها طراحی و به کار گرفته شده است. بدین منظور ابتدا پرندگان بر اساس وضعیت سلامتی به دو طبقهی سالم و بیمار تقسیم و صدای هر یک توسط یک میکروفن ثبت شد. سیگنالهای تحصیل شده توسط تبدیل سریع فوریه از حوزهی زمان به حوزه فرکانس انتقال یافتند. سپس 5 تابع ویژگی واریانس، انحراف از معیار، ریشهی میانگین مربعات، میانگین و کورتسیس از سیگنالهای حوزهی زمان و فرکانس استخراج شدند. از دو طبقهبند شبکه عصبی مصنوعی و ماشین بردار پشتیبان به منظور شناسایی سیگنالهای صدا استفاده شد. شبکه عصبی مصنوعی توانست به ترتیب با دقت 75 و 1/82 درصد و ماشین بردار پشتیبان نیز به ترتیب با دقت 7/85 و 8/92 درصد بر اساس سیگنالهای حوزه زمان و حوزهی فرکانس، سیگنالهای صدای مربوط به اردکهای بیمار و سالم را از یکدیگر تشخیص دهند. | ||
کلیدواژهها | ||
تشخیص پرندگان بیمار؛ سیگنالهای صدا؛ دادهکاوی؛ شبکه عصبی مصنوعی؛ ماشین بردار پشتیبان | ||
عنوان مقاله [English] | ||
Diagnosis of patients ducks based on their voices and using artificial intelligence methods | ||
نویسندگان [English] | ||
Ahmad Banakar1؛ Meghdad Khazaei2 | ||
چکیده [English] | ||
In this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. For this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. Gained signals were transformed from time-domain signal to frequency domain using Fast Fourier Transform (FFT). Then, 5 statistical features are extracted from both time and frequency signals namely, mean, standard division, root mean square, variance and kurtosis. Two classifiers which are artificial neuralnetworks (ANN) and support vector machine (SVM) are used, in order to acquire the bird classification in healthy and sick accuracy. The accuracy of ANN classifier in detection of healthy birds within sick and weak birds was determined 75% and 82.1 % based on the time and frequency domain of the sound signals, respectively. The accuracy of SVM classifier in detection of healthy birds within sick and weak birds was determined 85.7 % and 92.8 % based on the time and frequency domain of the sound signals, respectively. | ||
کلیدواژهها [English] | ||
sick birds’ detection, sound signals, Data Mining, artificial neural network (ANN), Support Vector Machine (SVM) | ||
مراجع | ||
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