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Article
Publication date: 1 December 2021

Hui Zhai, Wei Xiong, Fujin Li, Jie Yang, Dongyan Su and Yongjun Zhang

The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a…

Abstract

Purpose

The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.

Design/methodology/approach

In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.

Findings

The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.

Originality/value

A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

Details

Assembly Automation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0144-5154

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Article
Publication date: 11 November 2021

Sandeep Kumar Hegde and Monica R. Mundada

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio…

Abstract

Purpose

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

Design/methodology/approach

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

Findings

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Research limitations/implications

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

Practical implications

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Social implications

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

Originality/value

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 9 November 2021

Shilpa B L and Shambhavi B R

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is…

Abstract

Purpose

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.

Design/methodology/approach

This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.

Findings

The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.

Originality/value

This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 29 July 2014

Pinpin Qu

The mobile communication industry in China is vulnerable to competition, industry regulation, macroeconomy and so on, which leads to service income's volatility and…

Abstract

Purpose

The mobile communication industry in China is vulnerable to competition, industry regulation, macroeconomy and so on, which leads to service income's volatility and non-stationarity. Traditional income prediction models fail to take account of these factors, thus resulting in a low precision. The purpose of this paper is to to set up a new mobile communication service income prediction model based on grey system theory to overcome the inconformity between traditional models and qualitative analysis.

Design/methodology/approach

At first, mobile telecommunication service income is divided into number of users (NU) and average revenue per user (ARPU) prediction, respectively. Then, grey buffer operators are introduced to preprocess the time series according to their features and tendencies to eliminate the effect of shock disturbance. As a result, two grey models based on GM(1, 1) are constructed to forecast NU and ARPU, and thus the service income is obtained. At last, a case on Zhujiang mobile communication company is studied. The result proves that the proposed method is not only more accurate, but also could discover the turning point of income.

Findings

The results are convincing: it is more effective and accurate to employ grey buffer operator theory to predict the mobile communication service income compared with other methods. Besides, this method is applicable to cases with less data samples and faster development.

Practical implications

It's common to come across a system with less data and poor information. At this case, the grey prediction method exposed in the paper can be used to forecast the future trend which will give the predictors advice to achieve fine outcomes. Buffer operators can reduce the effect of shock disturbance and the GM(1, 1) model has the advantages of exploiting information using only a couple of data.

Originality/value

Considering the fast development of China's mobile communication in recent years, only limited data can be acquired to predict the future, which will definitely reduce the prediction precision using traditional models. The paper succeeds in introducing GM(1, 1) model based on grey buffer operators into the income prediction and the outcome proves that it has higher prediction precision and extensive application.

Details

Grey Systems: Theory and Application, vol. 4 no. 2
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 29 July 2014

Yinao Wang

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are…

Abstract

Purpose

The purpose of this paper is to discuss the interval forecasting, prediction interval and its reliability. When the predicted interval and its reliability are construction, the general rule which must satisfy is studied, grey wrapping band forecasting method is perfect.

Design/methodology/approach

A forecasting method puts forward a process of prediction interval. It also elaborates on the meaning of interval (the probability of the prediction interval including the real value of predicted variable). The general rule is abstracted and summarized by many forecasting cases. The general rule is discussed by axiomatic method.

Findings

The prediction interval is categorized into three types. Three axioms that construction predicted interval must satisfy are put forward. Grey wrapping band forecasting method is improved based on the proposed axioms.

Practical implications

Take the Shanghai composite index as the example, according to the K-line diagram from 4 January 2013 to 9 May 2013, the reliability of predicted rebound height of subsequent two or three trading day does not exceed the upper wrapping curve is 80 per cent. It is significant to understand the forecasting range correctly, build a reasonable range forecasting method and to apply grey wrapping band forecasting method correctly.

Originality/value

Grey wrapping band forecasting method is improved based on the proposed axioms.

Details

Grey Systems: Theory and Application, vol. 4 no. 2
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 15 October 2021

Shaobo Liang

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

Abstract

Purpose

This paper aims to explore the users' cross-app behavior characteristics in mobile search and to predict users' cross-app behavior using multi-dimensional information.

Design/methodology/approach

This paper presents a longitudinal user experiment in 15 days. This paper recruited 30 participants and collected their mobile phone log data in the whole experiment. The structured diary method was also used to collect contextual information in mobile search.

Findings

This study focused on the users' cross-app behavior in mobile search and described cross-app behavior's basic characteristics. Usage of communication app and tool apps could trigger more cross-app behavior in mobile search. The method of cross-app behavior prediction in the mobile search was proposed. Collecting users' more contextual information, such as search tasks, search motivation and other environmental information, can effectively improve the prediction accuracy of cross-app behavior in mobile search.

Practical implications

The future research on cross-app behavior prediction should focus on context information in mobile search. Better prediction of cross-app behavior can reduce the users' interaction burden.

Originality/value

This paper contributes to research into cross-app behavior, especially in the mobile search research domain.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

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Article
Publication date: 15 October 2021

Qiang Li, Sifeng Liu and Saad Ahmed Javed

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

Abstract

Purpose

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

Design/methodology/approach

A new two-stage multi-level equipment state classification system was proposed to forecast equipment operation status. The first stage involves predicting the equipment's normal state, and the second stage involves forecasting the equipment's abnormal status. Meanwhile, the equipment state classification is done according to the manufacturing company's internal specifications to define various equipment statuses. Then, the trouble state and waiting state were predicted by grey state prediction model.

Findings

A new two-stage multi-level equipment status classification system and a new approach for equipment states prediction has been proposed in this paper.

Practical implications

The application on a real-world case shown that the model is very effective for predicting equipment state. The equipment's major failure risk can be reduced significantly.

Originality/value

The proposed approach can help improve the effective prediction of the equipment's various operation states and reduce the equipment's major failure risk and thus maintenance costs.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Content available
Article
Publication date: 16 August 2021

Bo Qiu and Wei Fan

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been…

Abstract

Purpose

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

Design/methodology/approach

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

Findings

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

Originality/value

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Details

Smart and Resilient Transport, vol. 3 no. 2
Type: Research Article
ISSN: 2632-0487

Keywords

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Article
Publication date: 16 August 2021

Nur Azreen Zulkefly, Norjihan Abdul Ghani, Christie Pei-Yee Chin, Suraya Hamid and Nor Aniza Abdullah

Predicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance…

Abstract

Purpose

Predicting the impact of social entrepreneurship is crucial as it can help social entrepreneurs to determine the achievement of their social mission and performance. However, there is a lack of existing social entrepreneurship models to predict social enterprises' social impacts. This paper aims to propose the social impact prediction model for social entrepreneurs using a data analytic approach.

Design/methodology/approach

This study implemented an experimental method using three different algorithms: naive Bayes, k-nearest neighbor and J48 decision tree algorithms to develop and test the social impact prediction model.

Findings

The accurate result of the developed social impact prediction model is based on the list of identified social impact prediction variables that have been evaluated by social entrepreneurship experts. Based on the three algorithms' implementation of the model, the results showed that naive Bayes is the best performance classifier for social impact prediction accuracy.

Research limitations/implications

Although there are three categories of social entrepreneurship impact, this research only focuses on social impact. There will be a bright future of social entrepreneurship if the research can focus on all three social entrepreneurship categories. Future research in this area could look beyond these three categories of social entrepreneurship, so the prediction of social impact will be broader. The prospective researcher also can look beyond the difference and similarities of economic, social impacts and environmental impacts and study the overall perspective on those impacts.

Originality/value

This paper fulfills the need for the Malaysian social entrepreneurship blueprint to design the social impact in social entrepreneurship. There are none of the prediction models that can be used in predicting social impact in Malaysia. This study also contributes to social entrepreneur researchers, as the new social impact prediction variables found can be used in predicting social impact in social entrepreneurship in the future, which may lead to the significance of the prediction performance.

Details

Internet Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 6 July 2021

Xu Peng, Xiang Li and Xiao Yang

In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey…

Abstract

Purpose

In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey system theory to analyze the circular economy of the e-commerce market.

Design/methodology/approach

Construct a Grey system theory model, analyze the big data of e-commerce and circular economy of the e-commerce market and predict the development potential of China's e-commerce market.

Findings

The results show that the Grey system theory model can play an important role in the data analysis of circular economy of the e-commerce market.

Originality/value

Use Grey model to analyze e-commerce data, discover e-commerce market rules and problems and then optimize e-commerce market.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

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