2020
DOI: 10.1049/iet-cps.2019.0069
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CNN–SVM: a classification method for fruit fly image with the complex background

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Cited by 17 publications
(14 citation statements)
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“…In recent years, deep learning models have been gradually applied to intrusion detection to enhance the classification classifiers due to their high efficiency and easy implementation. Among deep learning models, CNNs have made great success in many fields [10][11][12], and researchers have applied CNNs in intrusion detection. Qian et al [13] analyzed the network traffic with a CNN.…”
Section: Related Workmentioning
confidence: 99%
“…In recent years, deep learning models have been gradually applied to intrusion detection to enhance the classification classifiers due to their high efficiency and easy implementation. Among deep learning models, CNNs have made great success in many fields [10][11][12], and researchers have applied CNNs in intrusion detection. Qian et al [13] analyzed the network traffic with a CNN.…”
Section: Related Workmentioning
confidence: 99%
“…Penelitian yang terkait dengan metode CNN-SVM, seperti klasifikasi lalat buah dengan background kompleks yang menghasilkan tingkat akurasi sebesar 92,04% [9], Penggunaan metode CNN-SVM sudah dilakukan di bidang lainnya seperti klasifikasi pengenalan angka tulisan tangan mendapatkan akurasi sebesar 94,40% [10], pengenalan bentuk 3D dengan ModelNet10 [11], diagnosa kesalahan bantalan dibawah kebisingan lingkungan dengan akurasi sebesar 99,5% [12], klasifikasi penginderaan jauh dengan tingkat akurasi sebesar 93,5% [13], pengenalan citra MRI dengan tingkat akurasi sebesar 99,5% [14], dan klasifikasi gambar dengan tingkat akurasi 90,72% [15].…”
Section: Pendahuluanunclassified
“…Arsitektur model CNN-SVM sendiri dirancang dengan mengganti lapisan keluaran terakhir model CNN-SVM. Model SVM memiliki akurasi klasifikasi serta kemampuan untuk generalisasi yang sangat baik [9]. Model CNN-SVM berbeda dengan CNN, dimana Softmax classifier dalam struktur CNN diganti dengan model pengkasifikasi SVM.…”
Section: Pendahuluanunclassified
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“…This method has also achieved excellent results in VOT [1,2]. In recent years, due to the excellent performance of convolutional neural network (CNN) features, it has been widely used in image classification [3], target recognition [4,5] and target detection [6]. Since the CNN is highly capable for feature extraction and expression, the application of the CNN to object tracking is highly significant for improving accuracy and robustness.…”
Section: Introductionmentioning
confidence: 99%