Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose

As the industry gradually enters the stage of unmanned and intelligent, factories in the future need to realize intelligent monitoring and diagnosis and maintenance of parts and components. In order to achieve this goal, it is first necessary to accurately identify and classify the parts in the factory. However, the existing literature rarely studies the classification and identification of parts of the entire factory. Due to the lack of existing data samples, this paper studies the identification and classification of small samples of industrial machine parts. In order to solve this problem, this paper establishes a convolutional neural network model based on the InceptionNet-V3 pretrained model through migration learning. Through experimental design, the influence of data expansion, learning rate and optimizer algorithm on the model effectiveness is studied, and the optimal model was finally determined, and the test accuracy rate reaches 99.74%. By comparing with the accuracy of other classifiers, the experimental results prove that the convolutional neural network model based on transfer learning can effectively solve the problem of recognition and classification of industrial machine parts with small samples and the idea of transfer learning can also be further promoted.

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Classification of neovascularization using convolutional neural network model

TELKOMNIKA (Telecommunication Computing Electronics and Control) ◽

10.12928/telkomnika.v17i1.11604 ◽

2019 ◽

Vol 17 (1) ◽

pp. 463 ◽

Cited By ~ 2

Author(s):

Wahyudi Setiawan ◽

Moh. Imam Utoyo ◽

Riries Rulaningtyas

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model

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Malware Classification with Improved Convolutional Neural Network Model

International Journal of Computer Network and Information Security ◽

10.5815/ijcnis.2020.06.03 ◽

2020 ◽

Vol 12 (6) ◽

pp. 30-43

Author(s):

Sumit S. Lad ◽

◽

Amol C. Adamuthe

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Personal Information ◽

Support Vector ◽

Svm Classifier ◽

Gray Scale ◽

Malware Classification

Malware is a threat to people in the cyber world. It steals personal information and harms computer systems. Various developers and information security specialists around the globe continuously work on strategies for detecting malware. From the last few years, machine learning has been investigated by many researchers for malware classification. The existing solutions require more computing resources and are not efficient for datasets with large numbers of samples. Using existing feature extractors for extracting features of images consumes more resources. This paper presents a Convolutional Neural Network model with pre-processing and augmentation techniques for the classification of malware gray-scale images. An investigation is conducted on the Malimg dataset, which contains 9339 gray-scale images. The dataset created from binaries of malware belongs to 25 different families. To create a precise approach and considering the success of deep learning techniques for the classification of raising the volume of newly created malware, we proposed CNN and Hybrid CNN+SVM model. The CNN is used as an automatic feature extractor that uses less resource and time as compared to the existing methods. Proposed CNN model shows (98.03%) accuracy which is better than other existing CNN models namely VGG16 (96.96%), ResNet50 (97.11%) InceptionV3 (97.22%), Xception (97.56%). The execution time of the proposed CNN model is significantly reduced than other existing CNN models. The proposed CNN model is hybridized with a support vector machine. Instead of using Softmax as activation function, SVM performs the task of classifying the malware based on features extracted by the CNN model. The proposed fine-tuned model of CNN produces a well-selected features vector of 256 Neurons with the FC layer, which is input to SVM. Linear SVC kernel transforms the binary SVM classifier into multi-class SVM, which classifies the malware samples using the one-against-one method and delivers the accuracy of 99.59%.

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