scholarly journals Implementasi Deep Learning Untuk Klasifikasi Citra Undertone Menggunakan Algoritma Convolutional Neural Network

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Rizka Fayyadhila ◽  
Apri Junaidi ◽  
Novian Adi Prasetyo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Color ◽  
Color Image ◽  
Feature Learning ◽  
Training Model ◽  
Training Data ◽  
Hair Color ◽  
Image Size ◽  
Learning Stage

The beauty of Indonesian women is distinguished by skin color, facial structure, hair color and body posture. For women today trying to look beautiful is a must. The way to make yourself look beautiful can be tricked by using make-up. But it's not that easy to use make-up because the type of make-up is differentiated based on the basic skin color, this is the problem for women in using make-up. Undertone is the basic color of the skin, there are three types of undertones, namely warm, cool and neutral. By knowing the type of undertone, it will make it easier for women to use make-up, namely to determine the appropriate shade based on the type of undertone. For this reason, a modeling of undertone image classification was made using the Convolutional Neural Network algorithm. This algorithm is claimed to be the best algorithm for solving object recognition and detection problems. The wrist vein color image dataset is required. The dataset used is 30 data per class, then preprocessing is carried out by homogenizing the image size to 64x64 pixels, then augmentation is carried out on each image by rotating and zooming. At this stage, the dataset will be divided into 3000 images which are divided into 80% training data and 20% testing data. Then it is processed through the convolution and pooling process at the feature learning stage, then the fully connected layer and classification stage where the feature learning results will be used for the classification process based on subclasses. Produces accuracy and training model values ​​reaching 98% with a loss value of 0.0214 and for accuracy from data validation it reaches 99% with a loss value of 0.0239 with model testing results of 99.5%.

scholarly journals Design Plant Disease Detection System Using Deep Learning Convolutional Neural Network

Khazanah ◽  
2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Xosya Salassa ◽  
◽  
Wais Al Qarni ◽  
Trional Novanza ◽  
Fahmi Guntara Diasa ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Plant Diseases ◽  
Training Data ◽  
Image Size ◽  
Average Model ◽  
Average Value ◽  
Batch Normalization

Indonesia is an agrarian country whose people mostly work in agriculture by contributing to the 3rd largest GDP. But on the other hand, the main problem in agriculture is the development of pests and diseases of crops. There are cases where there are crops that are attacked by diseases with less obvious symptoms for farmers. For example, in citrus plants that are attacked by CVPD. Initially, the citrus plant does not show too early symptoms of the disease, making it difficult to distinguish from healthy plants. Based on these problems early detection and identification of plant diseases are the main factors to prevent and reduce the spread of plant diseases. The study used deep learning methods with the Convolutional Neural Network (CNN) algorithm model. The dataset used comes from PlantVillage with a total of 20,639 leaf image files that have been classified based on their respective classes. The design of the model architecture is done by designing the CNN model following the DenseNet121 architecture, by changing the parameters to improve the accuracy results. Image size is 64, train shape (20639, 64, 64, 3), epoch value 50,100, and 150. The number of input layers used is 4 layers with shapes (64, 64, 3). Densenet121 shape (1024), global average pooling2D shape (1024), batch normalization 2 (1024), dropout (1024), dense (256), batch normalization 3 (256), root (Dense) (15). This research was conducted with 3 epoch iteration tests to find the best accuracy value. The training data for epoch 50,100, and 150 produces an average model accuracy of 99.38% and the average value of the model loss is 0.019% can also be seen from the testing data results for epoch 50,100, and 150 has an average model of 95.16% and can be seen also from the average value for the loss is 0.20%. Based on the algorithm that applied the resulting training accuracy of 99.58% and the accuracy of testing 96.41% then design this application is useful to accurately detect diseases in plants by using leaf imagery of the plant.

Cognitive Brain Tumour Segmentation Using Varying Window Architecture of Cascade Convolutional Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 21-29
Author(s):  
Mukesh Kumar Chandrakar ◽  
Anup Mishra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Tumour ◽  
Feature Space ◽  
Training Model ◽  
Research Area ◽  
Training Data ◽  
Tumour Segmentation ◽  
Specificity And Sensitivity ◽  
Brain Tumour Segmentation

Brain tumour segmentation is a growing research area in cognitive science and brain computing that helps the clinicians to plan the treatment as per the severity of the tumour cells or region. Accurate brain tumor detection requires measuring the volume, shape, boundaries, and other features. Deep learning is used to measure the characteristics without human intervention. The proper parameter setting and evaluation play a major role. Keeping this in mind, this paper focuses on varying window cascade architecture of convolutional neural network for brain tumour segmentation. The cognitive brain tumour computing is associated with the model using cognition concept for training data. The mixing of training data of different types of tumour images is applied to the model that ensures effective training. The feature space and training model improve the performance. The proposed architecture results in improvement in dice similarity, specificity, and sensitivity. The approach with improved performance is also compared with the existing approaches on the same dataset.

scholarly journals Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1688
Author(s):  
Luqman Ali ◽  
Fady Alnajjar ◽  
Hamad Al Jassmi ◽  
Munkhjargal Gochoo ◽  
Wasif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Concrete Structures ◽  
Model Performance ◽  
Training Data ◽  
Computational Time ◽  
Data Heterogeneity ◽  
Public Datasets ◽  
Detection And Localization

This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.

scholarly journals Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020 ◽  
Vol 10 (6) ◽  
pp. 2104
Author(s):  
Michał Tomaszewski ◽  
Paweł Michalski ◽  
Jakub Osuchowski
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Detection Efficiency ◽  
Training Data ◽  
Training Dataset ◽  
Training Set ◽  
Convolutional Network

This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

scholarly journals A Novel Convolutional Neural Network for Classifying Indian Coins by Denomination

2021 ◽  
Author(s):  
Yash Chauhan ◽  
Prateek Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Academic Research ◽  
High Volume ◽  
Classification Problem ◽  
General Purpose ◽  
Training Data ◽  
Slot Machines ◽  
Validation Data ◽  
Starting Point

Coins recognition systems have humungous applications from vending and slot machines to banking and management firms which directly translate to a high volume of research regarding the development of methods for such classification. In recent years, academic research has shifted towards a computer vision approach for sorting coins due to the advancement in the field of deep learning. However, most of the documented work utilizes what is known as ‘Transfer Learning’ in which we reuse a pre-trained model of a fixed architecture as a starting point for our training. While such an approach saves us a lot of time and effort, the generic nature of the pre-trained model can often become a bottleneck for performance on a specialized problem such as coin classification. This study develops a convolutional neural network (CNN) model from scratch and tests it against a widely-used general-purpose architecture known as Googlenet. We have shown in this study by comparing the performance of our model with that of Googlenet (documented in various previous studies) that a more straightforward and specialized architecture is more optimal than a more complex general architecture for the coin classification problem. The model developed in this study is trained and tested on 720 and 180 images of Indian coins of different denominations, respectively. The final accuracy gained by the model is 91.62% on the training data, while the accuracy is 90.55% on the validation data.

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

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