Convolutional neural network vs bag of features for bambara groundnut leaf disease recognition

This paper investigates bambara groundnut leaf disease recognition using two popular techniques known as Convolutional Neural Network (CNN) and Bag of Features (BOF) with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier. Leaf disease recognition has attracted many researchers because the outcome is useful for farmers. One of the crops that provide high income for farmers is bambara groundnut but the leaves are easily infected with diseases especially after the rain. This could affect the crop productivity. Thus, automatic disease recognition is crucial. A new dataset that consists of 400 images of the infected and non-infected leaves of bambara groundnut has been constructed. The experimental results indicate that both of these techniques produce excellent leaf disease recognition accuracy.

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Comparison of convolutional neural network and bag of features for multi-font digit recognition

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v15.i3.pp1322-1328 ◽

2019 ◽

Vol 15 (3) ◽

pp. 1322

Author(s):

Nasibah Husna Mohd Kadir ◽

Sharifah Nur Syafiqah Mohd Nur Hidayah ◽

Norasiah Mohammad ◽

Zaidah Ibrahim

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Character Recognition ◽

Recognition Accuracy ◽

Recognition Performance ◽

Support Vector ◽

Svm Classifier ◽

Learning Method ◽

Digit Recognition ◽

Bag Of Features

<span>This paper evaluates the recognition performance of Convolutional Neural Network (CNN) and Bag of Features (BoF) for multiple font digit recognition. Font digit recognition is part of character recognition that is used to translate images from many document-input tasks such as handwritten, typewritten and printed text. BoF is a popular machine learning method while CNN is a popular deep learning method. Experiments were performed by applying BoF with Speeded-up Robust Feature (SURF) and Support Vector Machine (SVM) classifier and compared with CNN on Chars74K dataset. The recognition accuracy produced by BoF is just slightly lower than CNN where the accuracy of CNN is 0.96 while the accuracy of BoF is 0.94.</span>

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Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

Frontiers in Public Health ◽

10.3389/fpubh.2021.670352 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ashwini K ◽

P. M. Durai Raj Vincent ◽

Kathiravan Srinivasan ◽

Chuan-Yu Chang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Infant Cry ◽

Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

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Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

The International Arab Journal of Information Technology ◽

10.34028/iajit/17/4/16 ◽

2020 ◽

Vol 17 (4) ◽

pp. 572-578

Author(s):

Mohammad Parseh ◽

Mohammad Rahmanimanesh ◽

Parviz Keshavarzi

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Support Vector ◽

Svm Classifier ◽

Handwritten Digit Recognition ◽

Digit Recognition ◽

Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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Classification of Medical Text Data Using Convolutional Neural Network-Support Vector Machine Method

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3042 ◽

2020 ◽

Vol 10 (7) ◽

pp. 1746-1753

Author(s):

Lan Liu ◽

Xiankun Sun ◽

Chengfan Li ◽

Yongmei Lei

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Text Data ◽

Machine Method ◽

Medical Text ◽

Conventional Methods

Conventional methods of medical text data classification, neglect of context among different words and semantic information, has a poor text description, classification effect and generalization capability and robustness. To tackle the inefficiencies and low precision in the classification of medical text data, in this paper, we presented a new classification method with improved convolutional neural network (CNN) and support vector machine (SVM), i.e., CNN-SVM method. In the method, some convolution kernel filters that contribute greatly to the CNN model are first selected by the average response energy (ARE) value, and then used to simplify and reconstruct the CNN model. Next, the SVM classifier was optimized by firefly algorithm (FA) and context information to overcome the disadvantages of over-saturation and over-training in SVM classification. Finally, the presented CNN-SVM method is tested by the simulation experiment and the true classification of medical text data. The experimental results show that the presented CNN-SVM method in this paper can significantly reduce the complexity and amount of computation compared to the conventional methods, and further promote the computational efficiency and classification accuracy of medical text data.

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Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification (Preprint)

10.2196/preprints.10513 ◽

2018 ◽

Author(s):

Youshan Zhang ◽

Jon-Patrick Allem ◽

Jennifer Beth Unger ◽

Tess Boley Cruz

Keyword(s):

Public Health ◽

Neural Network ◽

Social Media ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Public Health Surveillance ◽

Health Surveillance ◽

Support Vector ◽

Svm Classifier ◽

Tobacco Products

BACKGROUND Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images. OBJECTIVE This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image. METHODS Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. RESULTS As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. CONCLUSIONS A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies.

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Deep Convolutional Neural Network-Based Approaches for Face Recognition

Applied Sciences ◽

10.3390/app9204397 ◽

2019 ◽

Vol 9 (20) ◽

pp. 4397 ◽

Cited By ~ 10

Author(s):

Soad Almabdy ◽

Lamiaa Elrefaei

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Performance Optimization ◽

Large Scale ◽

Visual Recognition ◽

Recognition Accuracy ◽

Support Vector ◽

Svm Classifier ◽

In The Wild

Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.

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Single Channel EEG signal for Automatic Detection of Absence Seizure using Convolutional Neural Network

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122114608 ◽

2019 ◽

Vol 13 ◽

Author(s):

Niha Kamal Basha ◽

Aisha Banu Wahab

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Monitoring System ◽

Single Channel ◽

Confusion Matrix ◽

Sudden Change ◽

Automatic Detection ◽

Support Vector ◽

Absence Seizure

: Absence seizure is a type of brain disorder in which subject get into sudden lapses in attention. Which means sudden change in brain stimulation. Most of this type of disorder is widely found in children’s (5-18 years). These Electroencephalogram (EEG) signals are captured with long term monitoring system and are analyzed individually. In this paper, a Convolutional Neural Network to extract single channel EEG seizure features like Power, log sum of wavelet transform, cross correlation, and mean phase variance of each frame in a windows are extracted after pre-processing and classify them into normal or absence seizure class, is proposed as an empowerment of monitoring system by automatic detection of absence seizure. The training data is collected from the normal and absence seizure subjects in the form of Electroencephalogram. The objective is to perform automatic detection of absence seizure using single channel electroencephalogram signal as input. Here the data is used to train the proposed Convolutional Neural Network to extract and classify absence seizure. The Convolutional Neural Network consist of three layers 1] convolutional layer – which extract the features in the form of vector 2] Pooling layer – the dimensionality of output from convolutional layer is reduced and 3] Fully connected layer–the activation function called soft-max is used to find the probability distribution of output class. This paper goes through the automatic detection of absence seizure in detail and provide the comparative analysis of classification between Support Vector Machine and Convolutional Neural Network. The proposed approach outperforms the performance of Support Vector Machine by 80% in automatic detection of absence seizure and validated using confusion matrix.

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A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220987917 ◽

2021 ◽

pp. 014233122098791

Author(s):

Wanli Wang ◽

Botao Zhang ◽

Kaiqi Wu ◽

Sergey A Chepinskiy ◽

Anton A Zhilenkov ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Mobile Robots ◽

Convolutional Neural Network ◽

Hybrid Method ◽

Classification Accuracy ◽

Support Vector ◽

High Classification Accuracy ◽

Enhance Efficiency ◽

Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

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Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽

10.3390/met11040639 ◽

2021 ◽

Vol 11 (4) ◽

pp. 639

Author(s):

Chen Ma ◽

Haifei Dang ◽

Jun Du ◽

Pengfei He ◽

Minbo Jiang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Support Vector ◽

Defect Classification ◽

Droplet Deposition ◽

Network Support ◽

Visual Sensing ◽

Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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