scholarly journals Ensemble Learning Approaches Based on Covariance Pooling of CNN Features for High Resolution Remote Sensing Scene Classification

2020 ◽  
Vol 12 (20) ◽  
pp. 3292
Author(s):  
Sara Akodad ◽  
Lionel Bombrun ◽  
Junshi Xia ◽  
Yannick Berthoumieu ◽  
Christian Germain
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Ensemble Learning ◽  
Covariance Matrices ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Approaches ◽  
Scene Classification ◽  
First Order ◽  
Fisher Vector Encoding

Remote sensing image scene classification, which consists of labeling remote sensing images with a set of categories based on their content, has received remarkable attention for many applications such as land use mapping. Standard approaches are based on the multi-layer representation of first-order convolutional neural network (CNN) features. However, second-order CNNs have recently been shown to outperform traditional first-order CNNs for many computer vision tasks. Hence, the aim of this paper is to show the use of second-order statistics of CNN features for remote sensing scene classification. This takes the form of covariance matrices computed locally or globally on the output of a CNN. However, these datapoints do not lie in an Euclidean space but a Riemannian manifold. To manipulate them, Euclidean tools are not adapted. Other metrics should be considered such as the log-Euclidean one. This consists of projecting the set of covariance matrices on a tangent space defined at a reference point. In this tangent plane, which is a vector space, conventional machine learning algorithms can be considered, such as the Fisher vector encoding or SVM classifier. Based on this log-Euclidean framework, we propose a novel transfer learning approach composed of two hybrid architectures based on covariance pooling of CNN features, the first is local and the second is global. They rely on the extraction of features from models pre-trained on the ImageNet dataset processed with some machine learning algorithms. The first hybrid architecture consists of an ensemble learning approach with the log-Euclidean Fisher vector encoding of region covariance matrices computed locally on the first layers of a CNN. The second one concerns an ensemble learning approach based on the covariance pooling of CNN features extracted globally from the deepest layers. These two ensemble learning approaches are then combined together based on the strategy of the most diverse ensembles. For validation and comparison purposes, the proposed approach is tested on various challenging remote sensing datasets. Experimental results exhibit a significant gain of approximately 2% in overall accuracy for the proposed approach compared to a similar state-of-the-art method based on covariance pooling of CNN features (on the UC Merced dataset).

scholarly journals Emotion analysis of Arabic tweets using deep learning approach

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Massa Baali ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Media Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Approaches ◽  
Social Media Analysis ◽  
Deep Convolutional Neural Networks ◽  
Emotion Analysis ◽  
Classification Tasks

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

scholarly journals JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY

2021 ◽  
Vol V-3-2021 ◽  
pp. 317-324
Author(s):  
T. Stomberg ◽  
I. Weber ◽  
M. Schmitt ◽  
R. Roscher
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Cover ◽  
Observational Data ◽  
Satellite Imagery ◽  
Learning Approach ◽  
Scene Classification ◽  
Machine Learning Approach ◽  
New Knowledge

Abstract. Explainable machine learning has recently gained attention due to its contribution to understanding how a model works and why certain decisions are made. A so far less targeted goal, especially in remote sensing, is the derivation of new knowledge and scientific insights from observational data. In our paper, we propose an explainable machine learning approach to address the challenge that certain land cover classes such as wilderness are not well-defined in satellite imagery and can only be used with vague labels for mapping. Our approach consists of a combined U-Net and ResNet-18 that can perform scene classification while providing at the same time interpretable information with which we can derive new insights about classes. We show that our methodology allows us to deepen our understanding of what makes nature wild by automatically identifying simple concepts such as wasteland that semantically describes wilderness. It further quantifies a class’s sensitivity with respect to a concept and uses it as an indicator for how well a concept describes the class.

scholarly journals Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yijun Zhao ◽  
◽  
Tong Wang ◽  
Riley Bove ◽  
Bruce Cree ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
San Francisco ◽  
Ensemble Learning ◽  
Disease Onset ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Disease Course ◽  
Learning Techniques

AbstractThe rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

scholarly journals A Deep Learning Approach to Nightfire Detection based on Low-Light Satellite

2021 ◽  
Author(s):  
Yue Wang ◽  
Ye Ni ◽  
Xutao Li ◽  
Yunming Ye
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Early Detection ◽  
Remote Sensing Image ◽  
Experimental Results ◽  
Learning Approach ◽  
Learning Approaches ◽  
Low Light ◽  
Conventional Machine

Wildfires are a serious disaster, which often cause severe damages to forests and plants. Without an early detection and suitable control action, a small wildfire could grow into a big and serious one. The problem is especially fatal at night, as firefighters in general miss the chance to detect the wildfires in the very first few hours. Low-light satellites, which take pictures at night, offer an opportunity to detect night fire timely. However, previous studies identify night fires based on threshold methods or conventional machine learning approaches, which are not robust and accurate enough. In this paper, we develop a new deep learning approach, which determines night fire locations by a pixel-level classification on low-light remote sensing image. Experimental results on VIIRS data demonstrate the superiority and effectiveness of the proposed method, which outperforms conventional threshold and machine learning approaches.

scholarly journals Diagnosis of Leaf Disease in Cucurbita Gourd Family using Machine Learning Algorithms

2019 ◽  
Vol 8 (3) ◽  
pp. 6800-6804
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Powdery Mildew ◽  
Statistical Approach ◽  
Analysis Data ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Learning Approaches ◽  
Time Frequency ◽  
Future Work

PaperThe paper presents the implementation of various machines learning approach for the diagnosis of leaf diseases. For analysis data collection were done towards capturing the images of pumpkin leaf affected by different diseases. The pumpkin leaf samples were taken. The samples correspond to the blight and fungal problems like Alternaria, Powdery Mildew Anthracnose, and Yellow Vine Disease etc. The methods for analysis were implemented and tested which are based on time, frequency and statistical approach. For classification machine learning approaches like neural networks, SVM, KNN etc were analyzed. The implementation issues were presented for future work.

scholarly journals Performance Evaluation Of Machine Learning Techniques On Rpas Remote Sensing Images

2019 ◽  
Vol 8 (6S) ◽  
pp. 1035-1039
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Forest ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Kappa Index ◽  
Remote Sensing Images ◽  
Accuracy Measure

Recent advancements in remote sensing platforms from satellites to close-range Remotely Piloted Aircraft System (RPAS), is principal to a growing demand for innovative image processing and classification tools. Where, Machine learning approaches are very prevailing group of data driven implication tools that provide a broader scope when applied to remote sensed data. In this paper, applying different machine learning approaches on the remote sensing images with open source packages in R, to find out which algorithm is more efficient for obtaining better accuracy. We carried out a rigorous comparison of four machine learning algorithms-Support vector machine, Random forest, regression tree, Classification and Naive Bayes. These algorithms are evaluated by Classification accurateness, Kappa index and curve area as accuracy metrics. Ten runs are done to obtain the variance in the results on the training set. Using k-fold cross validation the validation is carried out. This theme identifies Random forest approach as the best method based on the accuracy measure under different conditions. Random forest is used to train efficient and highly stable with respect to variations in classification representation parameter values and significantly more accurate than other machine learning approaches trailed

scholarly journals Using Machine Learning for Remote Behaviour Classification—Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5426
Author(s):  
Lisa Giese ◽  
Jörg Melzheimer ◽  
Dirk Bockmühl ◽  
Bernd Wasiolka ◽  
Wanja Rast ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Direct Observations ◽  
Acceleration Data ◽  
Probability Threshold ◽  
Free Ranging

Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach

2019 ◽  
Author(s):  
Oskar Flygare ◽  
Jesper Enander ◽  
Erik Andersson ◽  
Brjánn Ljótsson ◽  
Volen Z Ivanov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Clinical Utility ◽  
Body Dysmorphic Disorder ◽  
Prediction Models ◽  
Behavioral Therapy ◽  
Learning Approach ◽  
Learning Approaches ◽  
Machine Learning Approach

**Background:** Previous attempts to identify predictors of treatment outcomes in body dysmorphic disorder (BDD) have yielded inconsistent findings. One way to increase precision and clinical utility could be to use machine learning methods, which can incorporate multiple non-linear associations in prediction models. **Methods:** This study used a random forests machine learning approach to test if it is possible to reliably predict remission from BDD in a sample of 88 individuals that had received internet-delivered cognitive behavioral therapy for BDD. The random forest models were compared to traditional logistic regression analyses. **Results:** Random forests correctly identified 78% of participants as remitters or non-remitters at post-treatment. The accuracy of prediction was lower in subsequent follow-ups (68%, 66% and 61% correctly classified at 3-, 12- and 24-month follow-ups, respectively). Depressive symptoms, treatment credibility, working alliance, and initial severity of BDD were among the most important predictors at the beginning of treatment. By contrast, the logistic regression models did not identify consistent and strong predictors of remission from BDD. **Conclusions:** The results provide initial support for the clinical utility of machine learning approaches in the prediction of outcomes of patients with BDD. **Trial registration:** ClinicalTrials.gov ID: NCT02010619.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

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