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. 

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 System of providing sustainability of tower cranes from overturn in extreme wind loads

2018 ◽  
Vol 224 ◽  
pp. 02086
Author(s):  
Pavel Sorokin ◽  
Alexey Mishin ◽  
Vitaliy Antsev ◽  
Alexey Red’kin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Network Models ◽  
Wind Loads ◽  
Neural Network Models ◽  
Quality Of Work ◽  
Development Stages ◽  
The Neural Network ◽  
Extreme Wind

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.

Comprehensive Introduction to Neural Networks

2021 ◽  
pp. 24-48
Author(s):  
Rajesh Sai K. ◽  
Veneela Adapa ◽  
Hari Kishan Kondaveeti
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Human Brain ◽  
Network Models ◽  
Neuron Models ◽  
Neural Network Models ◽  
The Neural Networks ◽  
Biological Neuron ◽  
Comprehensive Introduction

Unknowingly, artificial intelligence (AI) has become an inevitable part of our lives. In this chapter, the authors discuss how the neural networks, a sub-part of AI, changed the way we analyse things. In this chapter, the advent of neural networks, inspiration from the human brain, simplification models of biological neuron models are discussed. Later, a detailed overview of various neural network models, their strengths, limitations, applications, and challenges are presented in detail.

Implementation of Neural Network in Classifying the Product Quality for the ABS Metallization

2020 ◽  
Vol 305 ◽  
pp. 139-146
Author(s):  
Yuh Wen Chen ◽  
Sheng Chieh Wang ◽  
Pin Chuan Yao ◽  
Wen Tsung Lin ◽  
Aisyah Larasati ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Quality Control ◽  
Surface Treatment ◽  
Network Models ◽  
Acrylonitrile Butadiene Styrene ◽  
Operational Parameters ◽  
Neural Network Models ◽  
Quality Control Tool

The surface treatment conditions of a plastic surface are related to the quality of finished products. Usually, more than 20 causes dominate the success of electroplating for acrylonitrile butadiene styrene (ABS). Thus, the quality control is very complicated and challenging. Even nowadays, most of the production quality still relies on the operator's experience and intuition. This research takes a company of water hardware in Taiwan as the research object. We propose a revolutionary concept of quality management, combining artificial intelligence and surface treatment process altogether. We set up a parameter monitoring system during production to predict the quality of ABS metallization using neural network models such as artificial intelligence forms the basis of the intelligent manufacturing system. It can be used as a quality control tool to improve quality yield and industrial competitiveness. Totally 13 operational parameters (causes) and one quality parameter (consequence) of the electroplating tanks were collected from time to time to build the NN models. Interestingly, we finally find the fuzzy NN model performs better than the precise NN model. We conclude this is resulting from the limitation and vagueness of data.

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 EXPERIENCE OF NEURAL NETWORK MODELING IN MANAGING THE ACHIEVEMENT OF CRITERIA FOR A NEW MODEL OF A MEDICAL ORGANIZATION THAT USES LEAN TECHNOLOGIES

2020 ◽  
Vol 30 (5) ◽  
pp. 673-678
Author(s):  
S.B. Petrov ◽  
S.D. Mazunina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Performance Criteria ◽  
Neural Network Modeling ◽  
Neural Network Models ◽  
Medical Organization ◽  
New Model ◽  
Generalized Regression

Nowadays the scientific developments connected with increase of readiness of the medical institutions, rendering primary medical and sanitary aid, to work with application of methods and tools of lean technologies for increase of level of availability and quality of medical aid to the population of Russia acquire urgency. The aim of the study is to assess the prognostic importance of common neural network models to analyze the value components of the reception of a local therapist, affecting the level of satisfaction with the quality of medical care, from the position of management to achieve the criteria of a new model of a medical organization using lean technologies. The following types of neural network models were studied: based on a multilayer perceptron, a radial basis function, and a generalized regression neural network. Models based on multiple linear regression equations were used as a control group of networks. In total, 50 artificial neural networks were obtained and analyzed. The effectiveness of neural network models was evaluated based on the following parameters: the ratio of standard deviations of the forecast error and the source data, as well as the Pearson correlation between the observed and predicted indicators of the model. Among the studied neural network models, models based on a multi-layer perceptron and generalized regression neural networks have the highest quality of prediction, which makes them promising for use in systems that monitor and predict the structure of the value component of the main processes in medical organizations for patients. The proposed neural network models can become the basis for creating information management systems that monitor the achievement of performance criteria for a new model of a medical organization that uses lean technologies.

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.

scholarly journals APPLICATION OF COMPUTER VISION TECHNOLOGIES FOR THE DEVELOPMENT OF A MODEL FOR THE RECOGNITION OF LESIONS OF CULTIVATED PLANTS

2021 ◽  
Vol 21 (3) ◽  
pp. 5-13
Author(s):  
N.A. Yanishevskaya ◽  
◽  
I.P. Bolodurina ◽  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Network Models ◽  
Cultivated Plants ◽  
Industrial Complex ◽  
Neural Network Models ◽  
Crop Disease ◽  
The Russian Federation

In the Russian Federation, the agro-industrial complex is one of the leading sectors of the eco-nomy with a volume of domestic product of 4.5%. Russia owns 10 % of all arable land in the world. According to the data on the sown areas by crops in 2020, most of the agricultural area of Russia is occupied by wheat. The Russian Federation ranks third in the ranking of leading countries in the production of this type of grain crops, as well as leading positions in its export. Brown (leaf) and linear (stem) rust is the most harmful disease of grain crops. It is the reason for the sparseness of wheat crops and leads to a sharp decrease in yield. Therefore, one of the main tasks of farmers is to preserve the crop from diseases. The application of such areas of artificial intelligence as computer vision, machine learning and deep learning is able to cope with this task. These artificial intelligence technologies allow us to successfully solve applied problems of the agro-industrial complex using automated analysis of photographic materials. Aim. To consider the application of computer vision methods for the problem of classification of lesions of cultivated plants on the example of wheat. Materials and methods. The CGIAR Computer Vision for Crop Disease dataset for the crop disease recognition task is taken from the open source Kaggle. It is proposed to use an approach to the re-cognition of lesions of cultivated plants using the well-known neural network models ResNet50, DenseNet169, VGG16 and EfficientNet-B0. Neural network models receive images of wheat as in-put. The output of neural networks is the class of plant damage. To overcome the effect of overfit-ting neural networks, various regularization techniques are investigated. Results. The results of the classification quality, estimated by the software using the F1-score metric, which is the average harmonic between the Precision and Recall measures, are presented. Conclusion. As a result of the conducted research, it was found that the DenseNet model showed the best recognition accuracy us-ing a combination of transfer learning technology and DropOut and L2 regulation technologies to overcome the effect of retraining. The use of this approach allowed us to achieve a recognition ac-curacy of 91%.

scholarly journals Detailed Analysis of Top 10 AI- Deep Learning Neural Networks in Intrusion Detection for Internet of Things

2021 ◽  
Author(s):  
Kanimozhi V ◽  
T. Prem Jacob
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Network Models ◽  
Learning Models ◽  
Neural Network Models

Abstract Although there exist various strategies for IoT Intrusion Detection, this research article sheds light on the aspect of how the application of top 10 Artificial Intelligence - Deep Learning Models can be useful for both supervised and unsupervised learning related to the IoT network traffic data. It pictures the detailed comparative analysis for IoT Anomaly Detection on sensible IoT gadgets that are instrumental in detecting IoT anomalies by the usage of the latest dataset IoT-23. Many strategies are being developed for securing the IoT networks, but still, development can be mandated. IoT security can be improved by the usage of various deep learning methods. This exploration has examined the top 10 deep-learning techniques, as the realistic IoT-23 dataset for improving the security execution of IoT network traffic. We built up various neural network models for identifying 5 kinds of IoT attack classes such as Mirai, Denial of Service (DoS), Scan, Man in the Middle attack (MITM-ARP), and Normal records. These attacks can be detected by using a "softmax" function of multiclass classification in deep-learning neural network models. This research was implemented in the Anaconda3 environment with different packages such as Pandas, NumPy, Scipy, Scikit-learn, TensorFlow 2.2, Matplotlib, and Seaborn. The utilization of AI-deep learning models embraced various domains like healthcare, banking and finance, findings and scientific researches, and the business organizations along with the concepts like the Internet of Things. We found that the top 10 deep-learning models are capable of increasing the accuracy; minimize the loss functions and the execution time for building that specific model. It contributes a major significance to IoT anomaly detection by using emerging technologies Artificial Intelligence and Deep Learning Neural Networks. Hence the alleviation of assaults that happen on an IoT organization will be effective. Among the top 10 neural networks, Convolutional neural networks, Multilayer perceptron, and Generative Adversarial Networks (GANs) output the highest accuracy scores of 0.996317, 0.996157, and 0.995829 with minimized loss function and less time pertain to the execution. This article added to completely grasp the quirks of irregularity identification of IoT anomalies. Henceforth, this research analysis depicts the implementations of the Top 10 AI-deep learning models, which come in handy that assist you to perceive different neural network models and IoT anomaly detection better.

scholarly journals Technology for Cast Iron Microstructure Analysis in SIAMS Software Using Neural Networks

2021 ◽  
Author(s):  
Tatyana Sivkova ◽  
Aleksandr Gusev ◽  
Artem Syropyatov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cast Iron ◽  
Network Models ◽  
Neural Network Models ◽  
Microstructure Control ◽  
Key Issues ◽  
Training Neural Network ◽  
Metal And Alloys

The paper covers key issues of metal and alloys’ microstructure control using cast iron microstructure examples, and ways of resolving these issues by integration of neural networks into algorithms of SIAMS software. Paper lists key specifics of using the technology and training neural network, aimed at improving algorithm reproducibility, analysis acceleration and simplification. The method for training neural network models as part of the SIAMS software includes functionality for assessing the quality of training. The described method allows you control the model error using the value of the loss function. Developed algorithms in form of ready solutions were integrated into the SIAMS software package, and can be recommended for serial microstructure control in industrial laboratories.

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