A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

Download Full-text

A Hybrid Model (SVM-LOA) for Epileptic Seizure Detection in Long-Term EEG Records Using Machine Learning Techniques

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2018.1031.15 ◽

2018 ◽

Vol 11 (5) ◽

pp. 162-172

Author(s):

Mona Ali ◽

◽

Mohamed Abd-Elfattah ◽

Keyword(s):

Machine Learning ◽

Hybrid Model ◽

Epileptic Seizure ◽

Seizure Detection ◽

Machine Learning Techniques ◽

Epileptic Seizure Detection ◽

Learning Techniques

Download Full-text

Using Machine Learning for Decreasing State Uncertainty in Planning

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.11567 ◽

2020 ◽

Vol 69 ◽

pp. 765-806

Author(s):

Senka Krivic ◽

Michael Cashmore ◽

Daniele Magazzeni ◽

Sandor Szedmak ◽

Justus Piater

Keyword(s):

Machine Learning ◽

Active Learning ◽

Learning Process ◽

The State ◽

Machine Learning Techniques ◽

Planning Problem ◽

Planning Techniques ◽

Novel Approach ◽

Learning Techniques ◽

State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

Download Full-text

Classification and Generation of Microscopy Images With Plasmodium Falciparum Via Artificial Neural Networks

10.21203/rs.3.rs-46179/v1 ◽

2020 ◽

Author(s):

Rija Tonny Christian Ramarolahy ◽

Esther Opoku Gyasi ◽

Alessandro Crimi

Keyword(s):

Machine Learning ◽

Blood Smear ◽

Data Augmentation ◽

Small Sample ◽

Automated Detection ◽

Machine Learning Techniques ◽

Malaria Parasites ◽

Learning Techniques ◽

Synthetic Images ◽

Microscopy Images

Abstract Background: Recent studies use machine-learning techniques to detect parasites in microscopy images automatically. However, these tools are trained and tested in specific datasets. Indeed, even if over-fitting is avoided during the improvements of computer vision applications, large differences are expected. Differences might be related to settings of camera (exposure, white balance settings, etc) and different blood film slides preparation. Moreover, generative adversial networks offer new opportunities in microscopy: data homogenization, and increase of images in case of imbalanced or small sample size. Methods: Taking into consideration all those aspects, in this paper, we describe a more complete view including both detection and generating synthetic images: i) an automated detection used to detect malaria parasites on stained blood smear images using machine learning techniques testing several datasets. ii) investigate transfer learning and further testing in different unseen datasets having different staining, microscope, resolution, etc. iii) a generative approach to create synthetic images which can deceive experts. Results: The tested architecture achieved 0.98 and 0.95 area under the ROC curve in classifying images with respectively thin and thick smear. Moreover, the generated images proved to be very similar to the original and difficult to be distinguished by an expert microscopist, which identified correcly the real data for one dataset but had 50\% misclassification for another dataset of images. Conclusion: The proposed deep-learning architecture performed well on a classification task for malaria parasites classification. The automated detection for malaria can help the technician to reduce their work and do not need any presence of experts. Moreover, generative networks can also be applied to blood smear images to generate useful images for microscopists. Opening new ways to data augmentation, translation and homogenization.

Download Full-text