scholarly journals Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zeming Fan ◽  
Mudasir Jamil ◽  
Muhammad Tariq Sadiq ◽  
Xiwei Huang ◽  
Xiaojun Yu
Keyword(s):  
Early Detection ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Data Transfer ◽  
Training Data ◽  
Detection Accuracy ◽  
Boundary Segment ◽  
Learning Rates ◽  
Learning Techniques ◽  
Average Accuracy

Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and F-score of 97%, 96.5%, 97.5%, and 97%, respectively, which are higher than those of all other combinations. The proposed method is competitive with the available literature, demonstrating that it could be used for the early detection of COVID-19 patients.

scholarly journals Improving Semi-Supervised Learning for Audio Classification with FixMatch

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1807
Author(s):  
Sascha Grollmisch ◽  
Estefanía Cano
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Transfer Learning ◽  
Data Transfer ◽  
State Of The Art ◽  
Training Data ◽  
Audio Classification ◽  
Image Domain ◽  
Full Dataset ◽  
Audio Data

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

scholarly journals MRlogP: Transfer Learning Enables Accurate logP Prediction Using Small Experimental Training Datasets

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2029
Author(s):  
Yan-Kai Chen ◽  
Steven Shave ◽  
Manfred Auer
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mean Squared Error ◽  
Effective Property ◽  
Training Data ◽  
Limiting Factor ◽  
Attrition Rates ◽  
Learning Techniques ◽  
Trained Neural Network ◽  
Experimental Training

Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface.

scholarly journals Empirical Assessment of Transfer Learning Techniques for Surgical Tools Classification

2019 ◽  
Vol 9 (2S) ◽  
pp. 174-179
Keyword(s):  
Transfer Learning ◽  
Performance Metrics ◽  
Data Transfer ◽  
Image Annotation ◽  
Imbalanced Data ◽  
Large Datasets ◽  
Learning Curves ◽  
Training Images ◽  
Surgical Tool ◽  
Learning Techniques

Automated surgical tool classification in the medical images is a real-time computerized assistance for the surgeons in performing different operations. Deep learning has evolved in every facet of life due to availability of large datasets and emergence of Convolutional Neural Networks (CNN) that have paved the way for development of different image related processes. In the medical field there are number of challenges such as non-availability of datasets, image annotation requires extensive time, imbalanced data. Transfer learning is the process of applying existing pretrained models to the new problem. It is useful in those scenarios where the large datasets are not available, or the new dataset shares visual features with the existing dataset on which the model is pretrained. Most of the pretrained models are trained on ImageNet which is a largescale dataset (1.2 million labelled training images). In this paper we evaluated and explored two different CNN architectures namely VGG16 and MobileNet-v1-1.0-224 on subset of surgical toolset. This paper presents comparative analysis of the techniques using learning curves and different performance metrics.

scholarly journals Transfer Learning for Spam Text Classification

2021 ◽  
Vol 9 (VIII) ◽  
pp. 638-641
Author(s):  
Pratiksha Bongale
Keyword(s):  
Transfer Learning ◽  
Text Classification ◽  
Data Transfer ◽  
The Other ◽  
Training Data ◽  
Data Driven ◽  
Specific Training ◽  
Text Data ◽  
Domain Specific ◽  
Corpus Data

Today’s world is mostly data-driven. To deal with the humongous amount of data, Machine Learning and Data Mining strategies are put into usage. Traditional ML approaches presume that the model is tested on a dataset extracted from the same domain from where the training data has been taken from. Nevertheless, some real-world situations require machines to provide good results with very little domain-specific training data. This creates room for the development of machines that are capable of predicting accurately by being trained on easily found data. Transfer Learning is the key to it. It is the scientific art of applying the knowledge gained while learning a task to another task that is similar to the previous one in some or another way. This article focuses on building a model that is capable of differentiating text data into binary classes; one roofing the text data that is spam and the other not containing spam using BERT’s pre-trained model (bert-base-uncased). This pre-trained model has been trained on Wikipedia and Book Corpus data and the goal of this paper is to highlight the pre-trained model’s capabilities to transfer the knowledge that it has learned from its training (Wiki and Book Corpus) to classifying spam texts from the rest.

scholarly journals Enhancing Image Diagnosis by the Implementation of Transfer Classifiers

2019 ◽  
Vol 8 (3) ◽  
pp. 999-1002
Keyword(s):  
Supervised Learning ◽  
Transfer Learning ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Image Diagnosis ◽  
Sensitivity Problem ◽  
Common Application ◽  
Target Data

Images generated from a variety of sources and foundations today can pose difficulty for a user to interpret similarity in them or analyze them for further use because of their segmentation policies. This unconventionality can generate many errors, because of which the previously used traditional methodologies such as supervised learning techniques less resourceful, which requires huge quantity of labelled training data which mirrors the desired target data. This paper thus puts forward the mechanism of an alternative technique i.e. transfer learning to be used in image diagnosis so that efficiency and accuracy among images can be achieved. This type of mechanism deals with variation in the desired and actual data used for training and the outlier sensitivity, which ultimately enhances the predictions by giving better results in various areas, thus leaving the traditional methodologies behind. The following analysis further discusses about three types of transfer classifiers which can be applied using only small volume of training data sets and their contrast with the traditional method which requires huge quantities of training data having attributes with slight changes. The three different separators were compared amongst them and also together from the traditional methodology being used for a very common application used in our daily life. Also, commonly occurring problems such as the outlier sensitivity problem were taken into consideration and measures were taken to recognise and improvise them. On further research it was observed that the performance of transfer learning exceeds that of the conventional supervised learning approaches being used for small amount of characteristic training data provided reducing the stratification errors to a great extent

Towards Generalization of Intelligent Fault Detection for Roller Element Bearings via Distinct Dataset Transfer Learning

2021 ◽  
Author(s):  
Justin Larocque-Villiers ◽  
Patrick Dumond
Keyword(s):  
Fault Detection ◽  
Open Source ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Training Data ◽  
Fault Classification ◽  
Target Domain ◽  
Bearing Faults

Abstract Through the intelligent classification of bearing faults, predictive maintenance provides for the possibility of service schedule, inventory, maintenance, and safety optimization. However, real-world rotating machinery undergo a variety of operating conditions, fault conditions, and noise. Due to these factors, it is often required that a fault detection algorithm perform accurately even on data outside its trained domain. Although open-source datasets offer an incredible opportunity to advance the performance of predictive maintenance technology and methods, more research is required to develop algorithms capable of generalized intelligent fault detection across domains and discrepancies. In this study, current benchmarks on source–target domain discrepancy challenges are reviewed using the Case Western Reserve University (CWRU) and the Paderborn University (PbU) datasets. A convolutional neural network (CNN) architecture and data augmentation technique more suitable for generalization tasks is proposed and tested against existing benchmarks on the Pb U dataset by training on artificial faults and testing on real faults. The proposed method improves fault classification by 13.35%, with less than half the standard deviation of the compared benchmark. Transfer learning is then used to leverage the larger PbU dataset in order to make predictions on the CWRU dataset under a challenging source-target domain discrepancy in which there is minimal training data to adequately represent unseen bearing faults. The transfer learning-based CNN is found to be capable of generalizing across two open-source datasets, resulting in an improvement in accuracy from 53.1% to 68.3%.

Heterogeneous transfer learning for activity recognition using heuristic search techniques

2014 ◽  
Vol 10 (4) ◽  
pp. 393-418 ◽  
Author(s):  
Kyle Dillon Feuz ◽  
Diane J. Cook
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Heuristic Search ◽  
Care Facility ◽  
Feature Space ◽  
Training Data ◽  
Transfer Model ◽  
Content Type ◽  
Search Techniques ◽  
Learning Techniques

Purpose – The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations. Design/methodology/approach – This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines. Findings – The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy. Originality/value – The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

scholarly journals DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2743
Author(s):  
Kun Sun ◽  
Yi Liang ◽  
Xiaorui Ma ◽  
Yuanyuan Huai ◽  
Mengdao Xing
Keyword(s):  
Target Detection ◽  
Data Augmentation ◽  
State Of The Art ◽  
Low Cost ◽  
Detection Performance ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Computation Complexity ◽  
Learning Techniques

Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).

scholarly journals Detecting Pipeline Pathways in Landsat 5 Satellite Images with Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5642
Author(s):  
Jan Dasenbrock ◽  
Adam Pluta ◽  
Matthias Zech ◽  
Wided Medjroubi
Keyword(s):  
Satellite Images ◽  
Data Augmentation ◽  
System Modeling ◽  
Training Data ◽  
Transport Infrastructure ◽  
Data Sets ◽  
Low Resolution ◽  
Energy Infrastructure ◽  
Learning Rates ◽  
Future System

Energy system modeling is essential in analyzing present and future system configurations motivated by the energy transition. Energy models need various input data sets at different scales, including detailed information about energy generation and transport infrastructure. However, accessing such data sets is not straightforward and often restricted, especially for energy infrastructure data. We present a detection model for the automatic recognition of pipeline pathways using a Convolutional Neural Network (CNN) to address this lack of energy infrastructure data sets. The model was trained with historical low-resolution satellite images of the construction phase of British gas transport pipelines, made with the Landsat 5 Thematic Mapper instrument. The satellite images have been automatically labeled with the help of high-resolution pipeline route data provided by the respective Transmission System Operator (TSO). We have used data augmentation on the training data and trained our model with four different initial learning rates. The models trained with the different learning rates have been validated with 5-fold cross-validation using the Intersection over Union (IoU) metric. We show that our model can reliably identify pipeline pathways despite the comparably low resolution of the used satellite images. Further, we have successfully tested the model’s capability in other geographic regions by deploying satellite images of the NEL pipeline in Northern Germany.

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