scholarly journals Forecasting COVID-19 Pandemic in India and its Impact on Planet, People and Profit

2020 ◽  
Vol 8 (08) ◽  
pp. 414-422
Author(s):  
Om Patra ◽  
Dinesh Darshan ◽  
Elizabeth Abba
Keyword(s):  
Neural Network ◽  
Global Health ◽  
Indirect Effects ◽  
Prediction Method ◽  
Health Crisis ◽  
Deep Impact ◽  
The People ◽  
The Neural Network ◽  
Gated Recurrent Unit ◽  
Shed Light

The outbreak of coronavirus disease 2019 (COVID-19) has created a global health crisis that has had a deep impact on humanity. Due to the increasing number of daily cases it is a necessity to develop a prediction method. This paper aims at predicting the number of Coronavirus cases in India ahead of a month and also predicting individually some specific states/UT of India. Gated Recurrent Unit (GRU) and Facebook Prophet are used for prediction. GRU is used to show the accuracy of the neural network model and Facebook Prophet is used to predict the cases. At last the paper will elucidate the indirect effects of lockdown in India. This section will shed light on the indirect effects of the lockdown on people, planet and profit during the lockdown period taking account of the situation in India. This detailed annotation will build a foundation for the people of India to be prepared for upcoming days.

Optimization and Analysis for Coke Consumption Based on Clustering

2011 ◽  
Vol 287-290 ◽  
pp. 1112-1115
Author(s):  
Jun Hong Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Clustering Analysis ◽  
Coke Consumption ◽  
Prediction Method ◽  
Absolute Error ◽  
Coke Rate ◽  
The Neural Network ◽  
Fuel Rate

In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.

scholarly journals Axle Temperature Monitoring and Neural Network Prediction Analysis for High-Speed Train under Operation

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1662
Author(s):  
Wei Hao ◽  
Feng Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
High Speed ◽  
Prediction Method ◽  
High Speed Train ◽  
The Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Trained Neural Network

Predicting the axle temperature states of the high-speed train under operation in advance and evaluating working states of axle bearings is important for improving the safety of train operation and reducing accident risks. The method of monitoring the axle temperature of a train under operation, combined with the neural network prediction method, was applied. A total of 36 sensors were arranged at key positions such as the axle bearings of the train gearbox and the driving end of the traction motor. The positions of the sensors were symmetrical. Axle temperature measurements over 11 days with more than 38,000 km were obtained. The law of the change of the axle temperature in each section was obtained in different environments. The resultant data from the previous 10 days were used to train the neural network model, and a total of 800 samples were randomly selected from eight typical locations for the prediction of axle temperature over the following 3 min. In addition, the results predicted by the neural network method and the GM (1,1) method were compared. The results show that the predicted temperature of the trained neural network model is in good agreement with the experimental temperature, with higher precision than that of the GM (1,1) method, indicating that the proposed method is sufficiently accurate and can be a reliable tool for predicting axle temperature.

scholarly journals A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 432
Author(s):  
Bing Long ◽  
Kunping Wu ◽  
Pengcheng Li ◽  
Meng Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuel Cells ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Hydrogen Fuel Cells ◽  
Hydrogen Fuel ◽  
Starting Point ◽  
Useful Life ◽  
Gated Recurrent Unit

The remaining useful life (RUL) prediction for hydrogen fuel cells is an important part of its prognostics and health management (PHM). Artificial neural networks (ANNs) are proven to be very effective in RUL prediction, as they do not need to understand the failure mechanisms behind hydrogen fuel cells. A novel RUL prediction method for hydrogen fuel cells based on the gated recurrent unit ANN is proposed in this paper. Firstly, the data were preprocessed to remove outliers and noises. Secondly, the performance of different neural networks is compared, including the back propagation neural network (BPNN), the long short-term memory (LSTM) network and the gated recurrent unit (GRU) network. According to our proposed method based on GRU, the root mean square error was 0.0026, the mean absolute percentage error was 0.0038 and the coefficient of determination was 0.9891 for the data from the challenge datasets provided by FCLAB Research Federation, when the prediction starting point was 650 h. Compared with the other RUL prediction methods based on the BPNN and the LSTM, our prediction method is better in both prediction accuracy and convergence rate.

Decision Support System for Enhancing Crop Productivity

2020 ◽  
Vol 17 (9) ◽  
pp. 4438-4441
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Hrishikesh Salpekar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Neural Network Model ◽  
Arima Model ◽  
Market Price ◽  
Crop Productivity ◽  
Market Prices ◽  
The People ◽  
The Neural Network

Agriculture is the important aspect for the people of India. The life of large percentage of people in India is dependent on agriculture. The farmers are facing difficulty in selling their product to the markets due to lack of knowledge on crop prices. The market prices changes drastically in time. Using neural networks market price can be predicted and made available to the farmers to decide the time to sell their product. The ARIMA model is used to forecast the prices which can help the farmers to improve their economy and also the crop yield is predicted using neural network in the proposed system. So, that the user can check the yield of the crop in the particular piece of land before sowing. The prediction using the neural network model results in deciding the time to sell the prices and what will be the production of the crop over the year.

scholarly journals Fake News Detection in Machine Learning Hybrid Model

2020 ◽  
Vol 9 (1) ◽  
pp. 2668-2671
Keyword(s):  
Neural Network ◽  
Research Work ◽  
Fake News ◽  
Short Term ◽  
Political Issues ◽  
The Public ◽  
The People ◽  
Voting Decisions ◽  
False News ◽  
Gated Recurrent Unit

Now a day's prediction of fake news is somewhat an important aspect. The spreading of fake news mainly misleads the people and some false news that led to the absence of truth and stirs up the public opinion. It might influence some people in the society which leads to a loss in all directions like financial, psychological and also political issues, affecting voting decisions during elections etc. Our research work is to find reliable and accurate model that categorize a given news in dataset as fake or real. The existing techniques involved in are from a deep learning perspective by Recurrent Neural Network (RNN) technique models Vanilla, Gated Recurrent Unit (GRU) and Long Short-Term Memories (LSTMs) by applying on LAIR dataset. So we come up with a different plan to increase the accuracy by hybridizing Decision Tree and Random Forest.

Role of Social Media in COVID-19 Pandemic

2020 ◽  
Vol 4 (2) ◽  
pp. 59-60 ◽  
Author(s):  
Wajahat Hussain
Keyword(s):  
Social Media ◽  
Global Health ◽  
Spread Rate ◽  
Safety Measures ◽  
Health Crisis ◽  
Deep Impact ◽  
Sense Of Security ◽  
Physical Threat ◽  
Media Channels

Coronavirus disease 2019 (COVID-19) pandemic has produced a global health crisis that has had a deep impact on the way we perceive our world and everyday lives. Not only the spread rate of contagion and patterns of transmission endangered our sense of security, but the safety measures put in place to contain the spread of the virus also require social distancing by refraining from doing what is inherently human, which is to find comfort in the company of others. Within this context of physical threat, social and physical distancing, the role of the different mass media channels and social media in lives on individual, social and societal levels cannot be underestimated. 

ANALYSIS OF HISTORICAL DATA USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR PREDICTING THREATS IN COMPLEX DISTRIBUTED SYSTEMS

2021 ◽  
pp. 39-45
Author(s):  
E. V. Palchevsky ◽  
O. I. Khristodulo ◽  
S. V. Pavlov ◽  
A. V. Sokolova
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distributed Systems ◽  
Historical Data ◽  
Mathematical Formulation ◽  
Prediction Method ◽  
Point Of View ◽  
The Neural Network ◽  
Water Rise ◽  
Artificial Neural

A threat prediction method based on the mining of historical data in complex distributed systems is proposed. The relevance of the selected research topic is substantiated from the point of view of considering floods as a physical process of water rise, the level of which is measured at stationary hydrological posts. The mathematical formulation of the problem is formulated, within the framework of which an artificial neural network is implemented based on the free software library “TensorFlow”. An analysis of the effectiveness of the implemented artificial neural network was carried out, according to the results of which the weighted mean square-law deviation of the predicted water level value from the actual one when forecasting for one day at stationary hydrological posts was 0.032. Thus, the neural network allows predicting the flood situation with acceptable accuracy, which gives time for special services to carry out measures to counter this threat.

Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

2014 ◽  
Vol 538 ◽  
pp. 171-174 ◽  
Author(s):  
Jian Guo Cui ◽  
Long Zhang ◽  
Gui Hua Wang ◽  
Bo Cui ◽  
Li Ying Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Gas Turbine ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Health State ◽  
Object A ◽  
The Neural Network ◽  
Specific Object

Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

scholarly journals A Link Prediction Method Based on Neural Networks

2021 ◽  
Vol 11 (11) ◽  
pp. 5186
Author(s):  
Keping Li ◽  
Shuang Gu ◽  
Dongyang Yan
Keyword(s):  
Neural Network ◽  
Link Prediction ◽  
Prediction Models ◽  
Prediction Method ◽  
Network Evolution ◽  
Global Network ◽  
Network Efficiency ◽  
Structure Reliability ◽  
The Neural Network ◽  
Network Method

Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

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