An integrated approach based on Gaussian noises-based data augmentation method and AdaBoost model to predict faecal coliforms in rivers with small dataset

2021 ◽  
pp. 126510
Author(s):  
Ali El Bilali ◽  
Abdeslam Taleb ◽  
Moulay Abdellah Bahlaoui ◽  
Youssef Brouziyne
Keyword(s):  
Data Augmentation ◽  
Integrated Approach ◽  
Faecal Coliforms ◽  
Small Dataset

scholarly journals SPECIES IDENTIFICATION FOR AQUATIC BIOMONITORING USING DEEP RESIDUAL CNN AND TRANSFER LEARNING

2021 ◽  
Vol 20 (1) ◽  
pp. 001
Author(s):  
Aleksandar Milosavljević ◽  
Đurađ Milošević ◽  
Bratislav Predić
Keyword(s):  
Species Identification ◽  
Transfer Learning ◽  
Aquatic Insects ◽  
Aquatic Ecosystems ◽  
Data Augmentation ◽  
Ecological Status ◽  
Multiple Images ◽  
Public Datasets ◽  
Learning Data ◽  
Small Dataset

Aquatic insects and other benthic macroinvertebrates are mostly used as bioindicators of the ecological status of freshwaters. However, an expensive and time-consuming process of species identification represents one of the key obstacles for reliable biomonitoring of aquatic ecosystems. In this paper, we proposed a deep learning (DL) based method for species identification that we evaluated on several available public datasets (FIN-Benthic, STONEFLY9, and EPT29) along with our Chironomidae dataset (CHIRO10). The proposed method relies on three DL techniques used to improve robustness when training is done on a relatively small dataset: transfer learning, data augmentation, and feature dropout. We applied transfer learning by employing ResNet-50 deep convolutional neural network (CNN) pretrained on ImageNet 2012 dataset. The results show significant improvement compared to original contributions and confirms that there is a considerable gain when there are multiple images per specimen.

Data Augmentation Method of Small Dataset for Object Detection and Classification

2020 ◽  
Vol 15 (2) ◽  
pp. 184-189
Author(s):  
Jin Yong Kim ◽  
◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Object Detection ◽  
Data Augmentation ◽  
Small Dataset

scholarly journals Generative inter-class transformations for imbalanced data weather classification

2021 ◽  
Vol 2021 (1) ◽  
pp. 16-20
Author(s):  
Apostolia Tsirikoglou ◽  
Marcus Gladh ◽  
Daniel Sahlin ◽  
Gabriel Eilertsen ◽  
Jonas Unger
Keyword(s):  
Data Augmentation ◽  
Imbalanced Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Medical Applications ◽  
Single Image ◽  
Adversarial Networks ◽  
Augmentation Strategies ◽  
Future Work ◽  
Small Dataset

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.

scholarly journals Detection of Diabetic Retinopathy using Convolutional Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 1957-1960
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Data Augmentation ◽  
Medical Condition ◽  
Training Dataset ◽  
Fundus Images ◽  
Processor Unit ◽  
High Level ◽  
Graphics Processor Unit ◽  
Small Dataset

Diabetic Retinopathy is a medical condition in which damage occurs to the retina due to diabetes mellitus. The diagnosis of Diabetic Retinopathy through colored fundus images stand in need of experienced clinicians to identify the presence and significance of many small features, which makes it a time consuming task. In this paper, we propose a CNN based approach to detect Diabetic Retinopathy in fundus images. Data used to train the model is prepocessed by a new segmentation technique using Gabor filters. Due to small dataset, data augmentation is done to get enough data to train the model. Our segmentation model detects intricate features in the fundus images and detect the presence of DR. A high-end Graphics Processor Unit (GPU) is used to train the model efficiently. The publicly available Kaggle Dataset is used to demonstrate impressive results, particularly for a high-level classification task. On the training dataset of 14,650 images, our proposed CNN achieves a specificity of 94% and an accuracy of 69% on 3,660 validation images.

scholarly journals A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

2021 ◽  
Vol 13 (16) ◽  
pp. 8831
Author(s):  
Nicola Baldo ◽  
Matteo Miani ◽  
Fabio Rondinella ◽  
Clara Celauro
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Asphalt Concrete ◽  
Data Augmentation ◽  
Integrated Approach ◽  
Pavement Management ◽  
Stiffness Modulus ◽  
Heavy Weight ◽  
Concrete Layer ◽  
Augmentation Techniques

An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

scholarly journals Ship Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 63 ◽  
Author(s):  
Changchong Lu ◽  
Weihai Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Synthetic Aperture ◽  
Training Dataset ◽  
Classification Problems ◽  
Sar Images ◽  
Ship Classification ◽  
Small Dataset

Synthetic aperture radar (SAR) as an all-weather method of the remote sensing, now it has been an important tool in oceanographic observations, object tracking, etc. Due to advances in neural networks (NN), researchers started to study SAR ship classification problems with deep learning (DL) in recent years. However, the limited labeled SAR ship data become a bottleneck to train a neural network. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. To solve the problem of over-fitting which often appeared in training small dataset, we proposed a new method of data augmentation and combined it with transfer learning. Based on experiments and tests, the performance is evaluated. The results show that the types of the ships can be classified in high accuracies and reveal the effectiveness of our proposed method.

scholarly journals RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets

2020 ◽  
Vol 10 (11) ◽  
pp. 3861
Author(s):  
Marcel Sheeny ◽  
Andrew Wallace ◽  
Sen Wang
Keyword(s):  
Deep Learning ◽  
Physical Properties ◽  
Data Augmentation ◽  
Speckle Noise ◽  
Radar Data ◽  
Beam Divergence ◽  
Data Generation ◽  
Learning Models ◽  
Radar Signals ◽  
Small Dataset

We present a novel, parameterised radar data augmentation (RADIO) technique to generate realistic radar samples from small datasets for the development of radar-related deep learning models. RADIO leverages the physical properties of radar signals, such as attenuation, azimuthal beam divergence and speckle noise, for data generation and augmentation. Exemplary applications on radar-based classification and detection demonstrate that RADIO can generate meaningful radar samples that effectively boost the accuracy of classification and generalisability of deep models trained with a small dataset.

scholarly journals Enhancement of Multi-Class Structural Defect Recognition Using Generative Adversarial Network

2021 ◽  
Vol 13 (22) ◽  
pp. 12682
Author(s):  
Hyunkyu Shin ◽  
Yonghan Ahn ◽  
Sungho Tae ◽  
Heungbae Gil ◽  
Mihwa Song ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Detection Methods ◽  
Training Dataset ◽  
Building Structures ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Imbalance Problem ◽  
Augmentation Strategy ◽  
Defect Recognition ◽  
Small Dataset

Recently, in the building and infrastructure fields, studies on defect detection methods using deep learning have been widely implemented. For robust automatic recognition of defects in buildings, a sufficiently large training dataset is required for the target defects. However, it is challenging to collect sufficient data from degrading building structures. To address the data shortage and imbalance problem, in this study, a data augmentation method was developed using a generative adversarial network (GAN). To confirm the effect of data augmentation in the defect dataset of old structures, two scenarios were compared and experiments were conducted. As a result, in the models that applied the GAN-based data augmentation experimentally, the average performance increased by approximately 0.16 compared to the model trained using a small dataset. Based on the results of the experiments, the GAN-based data augmentation strategy is expected to be a reliable alternative to complement defect datasets with an unbalanced number of objects.

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