scholarly journals Weather Nowcasting Using Deep Learning Techniques

2021 ◽  
Author(s):  
Makhamisa Senekane ◽  
Mhlambululi Mafu ◽  
Molibeli Benedict Taele
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Weather Forecasting ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Precipitation Forecasting ◽  
Learning Techniques

Weather variations play a significant role in peoples’ short-term, medium-term or long-term planning. Therefore, understanding of weather patterns has become very important in decision making. Short-term weather forecasting (nowcasting) involves the prediction of weather over a short period of time; typically few hours. Different techniques have been proposed for short-term weather forecasting. Traditional techniques used for nowcasting are highly parametric, and hence complex. Recently, there has been a shift towards the use of artificial intelligence techniques for weather nowcasting. These include the use of machine learning techniques such as artificial neural networks. In this chapter, we report the use of deep learning techniques for weather nowcasting. Deep learning techniques were tested on meteorological data. Three deep learning techniques, namely multilayer perceptron, Elman recurrent neural networks and Jordan recurrent neural networks, were used in this work. Multilayer perceptron models achieved 91 and 75% accuracies for sunshine forecasting and precipitation forecasting respectively, Elman recurrent neural network models achieved accuracies of 96 and 97% for sunshine and precipitation forecasting respectively, while Jordan recurrent neural network models achieved accuracies of 97 and 97% for sunshine and precipitation nowcasting respectively. The results obtained underline the utility of using deep learning for weather nowcasting.

scholarly journals Demand Modelling in Telecommunications

10.14311/1121 ◽  
2009 ◽  
Vol 49 (2) ◽  
Author(s):  
M. Chvalina
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Term Forecast ◽  
Artificial Intelligence Methods ◽  
Demand Modelling

This article analyses the existing possibilities for using Standard Statistical Methods and Artificial Intelligence Methods for a short-term forecast and simulation of demand in the field of telecommunications. The most widespread methods are based on Time Series Analysis. Nowadays, approaches based on Artificial Intelligence Methods, including Neural Networks, are booming. Separate approaches will be used in the study of Demand Modelling in Telecommunications, and the results of these models will be compared with actual guaranteed values. Then we will examine the quality of Neural Network models. 

Data processing using deep learning of the generative-adversarial neural network (GAN)

2021 ◽  
Author(s):  
V.Y. Ilichev ◽  
I.V. Chukhraev
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Effective Means ◽  
Network Models ◽  
Neural Network Models ◽  
High Quality ◽  
Python Language ◽  
And Training

The article is devoted to the consideration of one of the areas of application of modern and promising computer technology – machine learning. This direction is based on the creation of models consisting of neural networks and their deep learning. At present, there is a need to generate new, not yet existing, images of objects of different types. Most often, text files or images act as such objects. To achieve a high quality of results, a generation method based on the adversarial work of two neural networks (generator and discriminator) was once worked out. This class of neural network models is distinguished by the complexity of topography, since it is necessary to correctly organize the structure of neural layers in order to achieve maximum accuracy and minimal error. The described program is created using the Python language and special libraries that extend the set of commands for performing additional functions: working with neural networks Keras (main library), integrating with the operating system Os, outputting graphs Matplotlib, working with data arrays Numpy and others. A description is given of the type and features of each neural layer, as well as the use of library connection functions, input of initial data, compilation and training of the obtained model. Next, the implementation of the procedure for outputting the results of evaluating the errors of the generator and discriminator and the accuracy achieved by the model depending on the number of cycles (eras) of its training is considered. Based on the results of the work, conclusions were drawn and recommendations were made for the use and development of the considered methodology for creating and training generative and adversarial neural networks. Studies have demonstrated the procedure for operating with comparatively simple and accessible, but effective means of a universal Python language with the Keras library to create and teach a complex neural network model. In fact, it has been proved that the use of this method allows to achieve high-quality results of machine learning, previously achievable only when using special software systems for working with neural networks.

scholarly journals Arabic text summarization using deep learning approach

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Molham Al-Maleh ◽  
Said Desouki
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Language Processing ◽  
Network Models ◽  
Arabic Language ◽  
Text Summarization ◽  
Neural Network Models ◽  
Data Set ◽  
Learning Techniques ◽  
Arabic Text Summarization

AbstractNatural language processing has witnessed remarkable progress with the advent of deep learning techniques. Text summarization, along other tasks like text translation and sentiment analysis, used deep neural network models to enhance results. The new methods of text summarization are subject to a sequence-to-sequence framework of encoder–decoder model, which is composed of neural networks trained jointly on both input and output. Deep neural networks take advantage of big datasets to improve their results. These networks are supported by the attention mechanism, which can deal with long texts more efficiently by identifying focus points in the text. They are also supported by the copy mechanism that allows the model to copy words from the source to the summary directly. In this research, we are re-implementing the basic summarization model that applies the sequence-to-sequence framework on the Arabic language, which has not witnessed the employment of this model in the text summarization before. Initially, we build an Arabic data set of summarized article headlines. This data set consists of approximately 300 thousand entries, each consisting of an article introduction and the headline corresponding to this introduction. We then apply baseline summarization models to the previous data set and compare the results using the ROUGE scale.

scholarly journals Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

2021 ◽  
Vol 21 (3) ◽  
pp. 175-188
Author(s):  
Sumaiya Thaseen Ikram ◽  
Aswani Kumar Cherukuri ◽  
Babu Poorva ◽  
Pamidi Sai Ushasree ◽  
Yishuo Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Network Models ◽  
Neural Network Models ◽  
Detection Model ◽  
Detection Systems ◽  
Learning Techniques

Abstract Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.

scholarly journals GMDH-type Neural Networks for Predicting Financial Time Series: A Study of Informational Efficiency of Stock Markets

2021 ◽  
Author(s):  
L Jakaite ◽  
M Ciemny ◽  
S Selitskiy ◽  
Vitaly Schetinin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Network Models ◽  
Efficient Market Hypothesis ◽  
Group Method ◽  
Informational Efficiency ◽  
Prediction Errors ◽  
Neural Network Models

Abstract A theory of Efficient Market Hypothesis (EMH) has been introduced by Fama to analyse financial markets. In particular the EMH theory has been proven in real cases under different conditions, including financial crises and frauds. The EMH assumes to examine the prediction accuracy of models designed on retrospective data. Such prediction models could be designed in different ways that motivated us to explore Machine Learning (ML) methods known for building models providing a high prediction performance. In this study we propose a ``deep'' learning method for building high-performance prediction models. The proposed method is based on the Group Method of Data Handling (GMDH) that is the deep learning paradigm capable of building multilayer neural-network models of a near-optimal complexity on given data. We show that the developed GMDH-type neural network has outperformed the models built by the conventional ML methods on the Warsaw Stock Exchange data. It is important that the complexity of the designed GMDH-type neural-networks is defined by the number of layers and connections between neurons. The performances of models were compared in terms of the prediction errors. We report a significantly smaller prediction error of the proposed method than that of the conventional autoregressive and "shallow’’ neural-network models. This finally allows us to conclude that traders will be advantaged by the proposed method.

scholarly journals A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

2018 ◽  
Vol 30 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Zhao Liu ◽  
Jianhua Guo ◽  
Jinde Cao ◽  
Yun Wei ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
Network Models ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Forecasting

It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

scholarly journals Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Jie Jiang ◽  
Qiuqiang Kong ◽  
Mark D. Plumbley ◽  
Nigel Gilbert ◽  
Mark Hoogendoorn ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Energy Consumption ◽  
Network Models ◽  
Binary Classifier ◽  
Neural Network Models ◽  
Learning Methods ◽  
Target Values ◽  
Energy Disaggregation

Energy disaggregation, a.k.a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.

scholarly journals Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 113-121
Author(s):  
Firman Pradana Rachman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

scholarly journals Next-generation of recurrent neural network models for cognition

2021 ◽  
Author(s):  
Guangyu Robert Yang ◽  
Manuel Molano-Mazon
Keyword(s):  
Neural Network ◽  
Recurrent Neural Networks ◽  
Network Models ◽  
Future Generations ◽  
Machine Learning Techniques ◽  
Cognitive Tasks ◽  
Next Generation ◽  
Neural Network Models ◽  
Learning Techniques ◽  
Essential Questions

Recurrent Neural Networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we discuss fundamental challenges faced by early work of this approach, and recent steps to overcome such challenges and build next-generation RNN models for cognition. We propose several essential questions that practitioners of this approach should address to continue building future generations of RNN models.

scholarly journals Time-evolving Text Classification with Deep Neural Networks

2018 ◽  
Author(s):  
Yu He ◽  
Jianxin Li ◽  
Yangqiu Song ◽  
Mutian He ◽  
Hao Peng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Text Classification ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Evolutionary Neural Network ◽  
Learned Features ◽  
Term Fluctuation

Traditional text classification algorithms are based on the assumption that data are independent and identically distributed. However, in most non-stationary scenarios, data may change smoothly due to long-term evolution and short-term fluctuation, which raises new challenges to traditional methods. In this paper, we present the first attempt to explore evolutionary neural network models for time-evolving text classification. We first introduce a simple way to extend arbitrary neural networks to evolutionary learning by using a temporal smoothness framework, and then propose a diachronic propagation framework to incorporate the historical impact into currently learned features through diachronic connections. Experiments on real-world news data demonstrate that our approaches greatly and consistently outperform traditional neural network models in both accuracy and stability.

Sign in / Sign up

Export Citation Format

Share Document