In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic mana...In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic management and effective utilization of network resources are the key factors of LTE models.Moreover,there are some major issues in LTE that are to be considered are effective load scheduling and traffic management.Through LTE is a depraved technology,it is been suffering from these issues.On addressing that,this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing(SMO-ELB).In this model,load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm.The simulation results show that the proposed model provides better results than exiting works in terms of efficiency,packet delivery ratio,Call Dropping Ratio(CDR)and Call Blocking Ratio(CBR).展开更多
Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecastin...Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities.The network will be trained and evaluated for accuracy with various sizes of data sets,i.e.weekly historical data of MCX,GOLD,COPPER and the results will be calculated.Design/methodology/approach-Desirable LSTM model for script price forecasting from the perspective of minimizing MSE.The approach which we have followed is shown below.(1)Acquire the Dataset.(2)Define your training and testing columns in the dataset.(3)Transform the input value using scalar.(4)Define the custom loss function.(5)Build and Compile the model.(6)Visualise the improvements in results.Findings-Financial series is one of the very aged techniques where a commerce person would commerce financial scripts,make business and earn some wealth from these companies that vend a part of their business on trading manifesto.Forecasting financial script prices is complex tasks that consider extensive human-computer interaction.Due to the correlated nature of financial series prices,conventional batch processing methods like an artificial neural network,convolutional neural network,cannot be utilised efficiently for financial market analysis.We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic(LSTM).The LSTM Classic is quite different from normal LSTM as it has customised loss function in it.This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure,and it helps forecast financial time series.Financial Series Index is the combination of various commodities(time series).This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/value-We had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset.For every epoch we can visualise the improvements in loss.One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts.Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.展开更多
文摘In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic management and effective utilization of network resources are the key factors of LTE models.Moreover,there are some major issues in LTE that are to be considered are effective load scheduling and traffic management.Through LTE is a depraved technology,it is been suffering from these issues.On addressing that,this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing(SMO-ELB).In this model,load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm.The simulation results show that the proposed model provides better results than exiting works in terms of efficiency,packet delivery ratio,Call Dropping Ratio(CDR)and Call Blocking Ratio(CBR).
文摘Purpose-We propose a Machine Learning(ML)approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities.The network will be trained and evaluated for accuracy with various sizes of data sets,i.e.weekly historical data of MCX,GOLD,COPPER and the results will be calculated.Design/methodology/approach-Desirable LSTM model for script price forecasting from the perspective of minimizing MSE.The approach which we have followed is shown below.(1)Acquire the Dataset.(2)Define your training and testing columns in the dataset.(3)Transform the input value using scalar.(4)Define the custom loss function.(5)Build and Compile the model.(6)Visualise the improvements in results.Findings-Financial series is one of the very aged techniques where a commerce person would commerce financial scripts,make business and earn some wealth from these companies that vend a part of their business on trading manifesto.Forecasting financial script prices is complex tasks that consider extensive human-computer interaction.Due to the correlated nature of financial series prices,conventional batch processing methods like an artificial neural network,convolutional neural network,cannot be utilised efficiently for financial market analysis.We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic(LSTM).The LSTM Classic is quite different from normal LSTM as it has customised loss function in it.This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure,and it helps forecast financial time series.Financial Series Index is the combination of various commodities(time series).This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected.This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/value-We had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset.For every epoch we can visualise the improvements in loss.One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts.Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.
