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1393/3/17، جلد ۱، شماره ۲، صفحات -
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژههای فارسی مقاله |
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| عنوان انگلیسی |
Aggregation Operators Enhance the Classification of ACL-Ruptured Knees Using Arthrometric Data |
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| چکیده انگلیسی مقاله |
Normal 0 false false false EN-US X-NONE AR-SA Many people suffer from the anterior cruciate ligament (ACL) injury, which can lead to knee instability associated with damage to other knee structures Purpose: In this study we present a classification method based on aggregation operators, using Adaptive Network-based Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) neural network to differentiate between arthrometric data of normal and ACL-ruptured knees. Methods: The data involves 132 samples consisting of 59 patients with injured knee and 73 normal subjects. ANFIS hybrid training algorithm is implemented using Fuzzy C-Means (FCM) and subtractive data clustering. The Levenberg–Marquardt (LM) training algorithm is used for MLP neural network. The results of ANFIS and MLP are then combined using aggregation operators. Results: The best accuracy (96%) is obtained by applying Choquet integral to the outputs of ANFIS classifier with the antecedent parameters selected using FCM algorithm. Conclusion: The experimental results show that aggregation operators enhance the outcomes of ANFIS and MLP classifiers in discriminating between ACL raptured knees and normal subjects. |
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| کلیدواژههای انگلیسی مقاله |
Anterior Cruciate Ligament, Knee Arthrometer, Classification, ANFIS, MLP, Aggregation Operators. |
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| نویسندگان مقاله |
51085---51086---51087--- |
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| نشانی اینترنتی |
http://fbt.tums.ac.ir/index.php/fbt/article/viewArticle/104 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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| نوع مقاله منتشر شده |
Original Articles |
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