Generating Rainfall Data using GANs

Rainfall prediction is one of the major discussions in the meteorology because it is a major factor on which many things in the environment rely on. Neural Nets or any other machine learning algorithms need very large amount of data in order to achieve better accuracy but sometimes data can be scarce, this type of problems can be resolved by using Generative Adversarial Networks. Generative Adversarial Networks which are known for generating data by using the existing features from the old data, like generating images etc. There are many types of datasets which are scarce, rainfall data in one among them. So, the proposed system generates the rainfall data using GAN. The generated data is used for training the classifier, which predicts the rainfall.

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Knowledge Discovery in Geographical Sciences—A Systematic Survey of Various Machine Learning Algorithms for Rainfall Prediction

10.1007/978-981-16-2597-8_51 ◽

2021 ◽

pp. 593-608

Author(s):

Sheikh Amir Fayaz ◽

Majid Zaman ◽

Muheet Ahmed Butt

Keyword(s):

Machine Learning ◽

Knowledge Discovery ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Systematic Survey ◽

Rainfall Prediction

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Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting

Machine Learning with Applications ◽

10.1016/j.mlwa.2021.100204 ◽

2021 ◽

pp. 100204

Author(s):

Ari Yair Barrera-Animas ◽

Lukumon Oladayo Oyedele ◽

Muhammad Bilal ◽

Taofeek Dolapo Akinosho ◽

Juan Manuel Davila Delgado ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Comparative Analysis ◽

Learning Algorithms ◽

Time Series Forecasting ◽

Machine Learning Algorithms ◽

Rainfall Prediction ◽

Modern Machine

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Improving ECG Classification Using Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i08.7037 ◽

2020 ◽

Vol 34 (08) ◽

pp. 13280-13285

Author(s):

Tomer Golany ◽

Gal Lavee ◽

Shai Tejman Yarden ◽

Kira Radinsky

Keyword(s):

Learning Algorithms ◽

Cardiac Activity ◽

Machine Learning Algorithms ◽

Training Data ◽

Generative Adversarial Networks ◽

Cardiac Events ◽

Ecg Signals ◽

Adversarial Networks ◽

Classifier Performance ◽

Supervised Learning Algorithms

The Electrocardiogram (ECG) is performed routinely by medical personell to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms. Numerous supervised learning algorithms were proposed, requiring manual feature extraction. Lately, deep neural networks were also proposed for this task for reaching state-of-the-art results. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations and the low amount of training data available for each arrhythmia are challenging for deep learning algorithms, and impede generalization. In this work, the use of generative adversarial networks is studied for the synthesis of ECG signals, which can then be used as additional training data to improve the classifier performance. Empirical results prove that the generated signals significantly improve ECG classification.

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Automated predictive analytics tool for rainfall forecasting

Scientific Reports ◽

10.1038/s41598-021-95735-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maulin Raval ◽

Pavithra Sivashanmugam ◽

Vu Pham ◽

Hardik Gohel ◽

Ajeet Kaushik ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Predictive Analytics ◽

Weather Conditions ◽

Machine Learning Algorithms ◽

Rainfall Data ◽

Machine Learning Techniques ◽

Rainfall Forecasting ◽

Rainfall Prediction ◽

Prediction Techniques

AbstractAustralia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

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PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.3301557 ◽

2019 ◽

Vol 33 ◽

pp. 557-564 ◽

Cited By ~ 8

Author(s):

Tomer Golany ◽

Kira Radinsky

Keyword(s):

Learning Algorithms ◽

Cardiac Activity ◽

Medical Personnel ◽

Machine Learning Algorithms ◽

Training Data ◽

Generative Adversarial Networks ◽

Patient Specific ◽

Cardiac Events ◽

Adversarial Networks ◽

Supervised Learning Algorithms

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting.

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Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

Applied Sciences ◽

10.3390/app8101949 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1949 ◽

Cited By ~ 10

Author(s):

Yagya Raj Pandeya ◽

Dongwhoon Kim ◽

Joonwhoan Lee

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Learning Algorithms ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Neural Nets ◽

Domestic Cat ◽

Neural Net ◽

Original Dataset ◽

Learned Features

The domestic cat (Feliscatus) is one of the most attractive pets in the world, and it generates mysterious kinds of sound according to its mood and situation. In this paper, we deal with the automatic classification of cat sounds using machine learning. Machine learning approach for the classification requires class labeled data, so our work starts with building a small dataset named CatSound across 10 categories. Along with the original dataset, we increase the amount of data with various audio data augmentation methods to help our classification task. In this study, we use two types of learned features from deep neural networks; one from a pre-trained convolutional neural net (CNN) on music data by transfer learning and the other from unsupervised convolutional deep belief network that is (CDBN) solely trained on a collected set of cat sounds. In addition to conventional GAP, we propose an effective pooling method called FDAP to explore a number of meaningful features. In FDAP, the frequency dimension is roughly divided and then the average pooling is applied in each division. For the classification, we exploited five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect following factors: the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, and the machine learning algorithms used for the classification. As expected, the proposed FDAP features with larger amount of data increased by augmentation combined with the ensemble approach have produced the best accuracy. Moreover, both learned features from pre-trained CNN and unsupervised CDBN produce good results in the experiment. Therefore, with the combination of all those positive factors, we obtained the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score.

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