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Open Access
Article
Publication date: 13 August 2020

Mariam AlKandari and Imtiaz Ahmad

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate…

10083

Abstract

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Open Access
Article
Publication date: 22 November 2022

Kedong Yin, Yun Cao, Shiwei Zhou and Xinman Lv

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems…

Abstract

Purpose

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Design/methodology/approach

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Findings

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

Originality/value

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Details

Marine Economics and Management, vol. 5 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Open Access
Article
Publication date: 24 October 2021

Piergiorgio Alotto, Paolo Di Barba, Alessandro Formisano, Gabriele Maria Lozito, Raffaele Martone, Maria Evelina Mognaschi, Maurizio Repetto, Alessandro Salvini and Antonio Savini

Inverse problems in electromagnetism, namely, the recovery of sources (currents or charges) or system data from measured effects, are usually ill-posed or, in the numerical…

Abstract

Purpose

Inverse problems in electromagnetism, namely, the recovery of sources (currents or charges) or system data from measured effects, are usually ill-posed or, in the numerical formulation, ill-conditioned and require suitable regularization to provide meaningful results. To test new regularization methods, there is the need of benchmark problems, which numerical properties and solutions should be well known. Hence, this study aims to define a benchmark problem, suitable to test new regularization approaches and solves with different methods.

Design/methodology/approach

To assess reliability and performance of different solving strategies for inverse source problems, a benchmark problem of current synthesis is defined and solved by means of several regularization methods in a comparative way; subsequently, an approach in terms of an artificial neural network (ANN) is considered as a viable alternative to classical regularization schemes. The solution of the underlying forward problem is based on a finite element analysis.

Findings

The paper provides a very detailed analysis of the proposed inverse problem in terms of numerical properties of the lead field matrix. The solutions found by different regularization approaches and an ANN method are provided, showing the performance of the applied methods and the numerical issues of the benchmark problem.

Originality/value

The value of the paper is to provide the numerical characteristics and issues of the proposed benchmark problem in a comprehensive way, by means of a wide variety of regularization methods and an ANN approach.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Open Access
Article
Publication date: 3 August 2020

Djordje Cica, Branislav Sredanovic, Sasa Tesic and Davorin Kramar

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with…

2025

Abstract

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

Details

Applied Computing and Informatics, vol. 20 no. 1/2
Type: Research Article
ISSN: 2634-1964

Keywords

Open Access
Article
Publication date: 14 March 2016

Valeria Croce

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is…

3490

Abstract

Purpose

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Design/methodology/approach

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Findings

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Practical implications

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

Originality/value

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

Open Access
Article
Publication date: 5 July 2022

Paraskevi Gatzioufa and Vaggelis Saprikis

Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt…

8573

Abstract

Purpose

Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.

Design/methodology/approach

The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.

Findings

After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.

Originality/value

As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Open Access
Article
Publication date: 17 July 2023

Abhishek Vashishth, Bart Alex Lameijer, Ayon Chakraborty, Jiju Antony and Jürgen Moormann

The purpose of this paper is to contribute to the limited body of empirical knowledge on the impact of Lean Six Sigma (LSS) program implementations on organizational performance…

1652

Abstract

Purpose

The purpose of this paper is to contribute to the limited body of empirical knowledge on the impact of Lean Six Sigma (LSS) program implementations on organizational performance in financial services by investigating how antecedents of Lean Six Sigma program success (motivations, selected LSS methods and challenges) affect organizational performance enhancement via LSS program performance.

Design/methodology/approach

A sample of 198 LSS professionals from 7 countries are surveyed. Structural equation modeling (SEM) is performed to test the questioned relations.

Findings

This study’s findings comprise: (1) LSS program performance partially mediates the relationship between motivations for LSS implementation and organizational performance, (2) selected LSS method applications has a fully (mediated) indirect impact on organizational performance, (3) LSS implementation challenges also have an indirect (mediated) impact on organizational performance and (4) LSS program performance has a positive impact on organizational performance.

Originality/value

The findings of this research predominantly provide nuances and details about LSS implementation antecedents and effects, useful for managers in advising their business leaders about the prerequisites and potential operational and financial benefits of LSS implementation. Furthermore, the paper provides evidence and details about the relationship between important antecedents for LSS implementation identified in existing literature and their impact on organizational performance in services. Thereby, this research is the first in providing empirical, cross-sectional, evidence for the antecedents and effects of LSS program implementations in financial services.

Details

International Journal of Quality & Reliability Management, vol. 41 no. 2
Type: Research Article
ISSN: 0265-671X

Keywords

Open Access
Article
Publication date: 13 March 2024

Tjaša Redek and Uroš Godnov

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that…

Abstract

Purpose

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Design/methodology/approach

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

Findings

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

Research limitations/implications

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

Originality/value

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

Details

Kybernetes, vol. 53 no. 13
Type: Research Article
ISSN: 0368-492X

Keywords

Open Access
Article
Publication date: 5 March 2021

Xuan Ji, Jiachen Wang and Zhijun Yan

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with…

16370

Abstract

Purpose

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

Design/methodology/approach

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

Findings

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

Originality/value

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

Details

International Journal of Crowd Science, vol. 5 no. 1
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 5 November 2018

Kristian Johan Sund, Stuart Barnes and Jan Mattsson

The recently developed resource orchestration theory studies the processes by which managers handle resources to create competitive advantages. According to this theory, it is the…

8622

Abstract

Purpose

The recently developed resource orchestration theory studies the processes by which managers handle resources to create competitive advantages. According to this theory, it is the way that resources interact with each other that results in such advantages. Resource integration, i.e. the alignment, or fit between resources, is one important outcome of resource orchestration processes. This paper aims to develop a scale and outline approaches to measuring such resource integration.

Design/methodology/approach

Using a typology of five types of resources derived from value theory, the authors develop a scale for measuring the fit between resource types, i.e. the degree of resource integration. The authors illustrate the method using a case example of an IT company and demonstrate how a variety of statistical methods including hierarchical cluster analysis, structural equation modeling, social network analysis and methods from biostatistics can provide measures of resource integration.

Findings

The authors develop a scale and associated measures that can help scholars systematically measure and identify firms with a high or low level of resource integration capability. This makes it possible to investigate further these companies and reconstruct how they support dynamic capabilities, as well as commonalities across firms with high and low levels of this capability.

Originality/value

Existing studies on resource orchestration have failed to provide us with a reliable measurement instrument that can be used both in cross-sectional work, and in repeated or time-series studies, allowing us to assess the degree to which a wider range of resources in an organization are integrated. The authors develop and demonstrate such an instrument.

Details

International Journal of Organizational Analysis, vol. 26 no. 5
Type: Research Article
ISSN: 1934-8835

Keywords

1 – 10 of over 5000