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Mushroom Toxicity Recognition Based on Multigrained Cascade Forest
المؤلفون المشاركون
Yang, Xiuhong
Wang, Yingying
Zhang, Hongbo
Du, Jixiang
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-01
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Due to the tastiness of mushroom, this edible fungus often appears in people’s daily meals.
Nevertheless, there are still various mushroom species that have not been identified.
Thus, the automatic identification of mushroom toxicity is of great value.
A number of methods are commonly employed to recognize mushroom toxicity, such as folk experience, chemical testing, animal experiments, and fungal classification, all of which cannot produce quick, accurate results and have a complicated cycle.
To solve these problems, in this paper, we proposed an automatic toxicity identification method based on visual features.
The proposed method regards toxicity identification as a binary classification problem.
First, intuitive and easily accessible appearance data, such as the cap shape and color of mushrooms, were taken as features.
Second, the missing data in any of the features were handled in two ways.
Finally, three pattern-recognition methods, including logistic regression, support vector machine, and multigrained cascade forest, were used to construct 3 different toxicity classifiers for mushrooms.
Compared with the logistic regression and support vector machine classifiers, the multigrained cascade forest classifier had better performance with an accuracy of approximately 98%, enhancing the possibility of preventing food poisoning.
These classifiers can recognize the toxicity of mushrooms—even that of some unknown species—according to their appearance features and important social and application value.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Yingying& Du, Jixiang& Zhang, Hongbo& Yang, Xiuhong. 2020. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Yingying…[et al.]. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Yingying& Du, Jixiang& Zhang, Hongbo& Yang, Xiuhong. Mushroom Toxicity Recognition Based on Multigrained Cascade Forest. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1209211
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1209211
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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