scholarly journals Automated spectroscopic modelling with optimised convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zefang Shen ◽  
R. A. Viscarra Rossel
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Random Search ◽  
Parametric Representation ◽  
Optimal Model ◽  
One Dimensional ◽  
Importance Analysis ◽  
Spectroscopic Database ◽  
Model Training ◽  
Shed Light

AbstractConvolutional neural networks (CNN) for spectroscopic modelling are currently tuned manually, and the effects of their hyperparameters are not analysed. These can result in sub-optimal models. Here, we propose an approach to tune one-dimensional CNN (1D-CNNs) automatically. It consists of a parametric representation of 1D-CNNs and an optimisation of hyperparameters to maximise a model’s performance. We used a large European soil spectroscopic database to demonstrate our approach for estimating soil organic carbon (SOC) contents. To assess the optimisation, we compared it to random search, and to understand the effects of the hyperparameters, we calculated their importance using functional Analysis of Variance. Compared to random search, the optimisation produced better final results and showed faster convergence. The optimal model produced the most accurate estimates of SOC with $$\hbox {RMSE} = 9.67 \pm 0.51$$ RMSE = 9.67 ± 0.51 (s.d.) and $${R}^2 = 0.89 \pm 0.013$$ R 2 = 0.89 ± 0.013 (s.d.). The hyperparameters associated with model training and architecture critically affected the model’s performance, while those related to the spectral preprocessing had little effect. The optimisation searched through a complex hyperparameter space and returned an optimal 1D-CNN. Our approach simplified the development of 1D-CNNs for spectroscopic modelling by automatically selecting hyperparameters and preprocessing methods. Hyperparameter importance analysis shed light on the tuning process and increased the model’s reliability.

scholarly journals One-dimensional convolutional neural networks for low/high arousal classification from electrodermal activity

2022 ◽  
Vol 71 ◽  
pp. 103203
Author(s):  
Roberto Sánchez-Reolid ◽  
Francisco López de la Rosa ◽  
María T. López ◽  
Antonio Fernández-Caballero
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Electrodermal Activity ◽  
High Arousal ◽  
One Dimensional

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

2021 ◽  
Vol 5 (2) ◽  
pp. 312-318
Author(s):  
Rima Dias Ramadhani ◽  
Afandi Nur Aziz Thohari ◽  
Condro Kartiko ◽  
Apri Junaidi ◽  
Tri Ginanjar Laksana ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computer Science ◽  
No Value ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Indonesian Government ◽  
Speed Up ◽  
Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

scholarly journals AN APPROACH TO ACCELERATE THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BY TUNING THE HYPERPARAMETERS OF LEARNING

2020 ◽  
Vol 2 (2) ◽  
pp. 32-37
Author(s):  
P. RADIUK ◽  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Processing ◽  
Network Architecture ◽  
Random Search ◽  
Machine Learning Algorithms ◽  
Language Recognition ◽  
Data Set ◽  
Training Time

Over the last decade, a set of machine learning algorithms called deep learning has led to significant improvements in computer vision, natural language recognition and processing. This has led to the widespread use of a variety of commercial, learning-based products in various fields of human activity. Despite this success, the use of deep neural networks remains a black box. Today, the process of setting hyperparameters and designing a network architecture requires experience and a lot of trial and error and is based more on chance than on a scientific approach. At the same time, the task of simplifying deep learning is extremely urgent. To date, no simple ways have been invented to establish the optimal values of learning hyperparameters, namely learning speed, sample size, data set, learning pulse, and weight loss. Grid search and random search of hyperparameter space are extremely resource intensive. The choice of hyperparameters is critical for the training time and the final result. In addition, experts often choose one of the standard architectures (for example, ResNets and ready-made sets of hyperparameters. However, such kits are usually suboptimal for specific practical tasks. The presented work offers an approach to finding the optimal set of hyperparameters of learning ZNM. An integrated approach to all hyperparameters is valuable because there is an interdependence between them. The aim of the work is to develop an approach for setting a set of hyperparameters, which will reduce the time spent during the design of ZNM and ensure the efficiency of its work. In recent decades, the introduction of deep learning methods, in particular convolutional neural networks (CNNs), has led to impressive success in image and video processing. However, the training of CNN has been commonly mostly based on the employment of quasi-optimal hyperparameters. Such an approach usually requires huge computational and time costs to train the network and does not guarantee a satisfactory result. However, hyperparameters play a crucial role in the effectiveness of CNN, as diverse hyperparameters lead to models with significantly different characteristics. Poorly selected hyperparameters generally lead to low model performance. The issue of choosing optimal hyperparameters for CNN has not been resolved yet. The presented work proposes several practical approaches to setting hyperparameters, which allows reducing training time and increasing the accuracy of the model. The article considers the function of training validation loss during underfitting and overfitting. There are guidelines in the end to reach the optimization point. The paper also considers the regulation of learning rate and momentum to accelerate network training. All experiments are based on the widespread CIFAR-10 and CIFAR-100 datasets.

scholarly journals Lung X-ray Segmentation using Deep Convolutional Neural Networks on Contrast-Enhanced Binarized Images

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 545 ◽  
Author(s):  
Hsin-Jui Chen ◽  
Shanq-Jang Ruan ◽  
Sha-Wo Huang ◽  
Yan-Tsung Peng
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Processing Approach ◽  
Segmentation Accuracy ◽  
Enhancement Method ◽  
Digital Chest ◽  
Chest X Ray ◽  
Model Training

Automatically locating the lung regions effectively and efficiently in digital chest X-ray (CXR) images is important in computer-aided diagnosis. In this paper, we propose an adaptive pre-processing approach for segmenting the lung regions from CXR images using convolutional neural networks-based (CNN-based) architectures. It is comprised of three steps. First, a contrast enhancement method specifically designed for CXR images is adopted. Second, adaptive image binarization is applied to CXR images to separate the image foreground and background. Third, CNN-based architectures are trained on the binarized images for image segmentation. The experimental results show that the proposed pre-processing approach is applicable and effective to various CNN-based architectures and can achieve comparable segmentation accuracy to that of state-of-the-art methods while greatly expediting the model training by up to 20.74 % and reducing storage space for CRX image datasets by down to 94.6 % on average.

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