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Open Access
Article
Publication date: 8 December 2023

Armin Mahmoodi, Leila Hashemi, Amin Mahmoodi, Benyamin Mahmoodi and Milad Jasemi

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese…

Abstract

Purpose

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

Design/methodology/approach

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Findings

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

Research limitations/implications

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

Originality/value

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Details

Journal of Capital Markets Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-4774

Keywords

Open Access
Article
Publication date: 30 May 2024

Nadja Fugleberg Damtoft, Dennis van Liempd and Rainer Lueg

Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements…

Abstract

Purpose

Researchers and practitioners have recently been interested in corporate sustainability performance (CSP). However, knowledge on measuring CSP is limited. Many CSP-measurements are eclectic, without guidance for contextual applications. This paper aims to develop a conceptual framework that categorizes, explains and evaluates measurements based on their accuracy and precision and provides a guideline for their context-specific application.

Design/methodology/approach

The authors conducted a systematic literature review of an initial sample of 1,415 papers.

Findings

The final sample of 74 papers suggested four measurement categories: isolated indicators, indicator frameworks, Sustainability Balanced Scorecards (SBSC) and Sustainability Performance Measurement Systems (SPMS). The analysis reveals that isolated indicators are inaccurate and imprecise, limiting their application to organizations with delimited, specific measurements of parts of CSP due to the risk of a GIGO-effect (i.e. low-quality input will always produce low-quality output). CSP-indicator frameworks are imprecise but accurate, making them applicable to organizations that handle a more significant amount of CSP data. They have a risk of greensplashing, i.e. many indicators not connected to the industry, organization or strategy. In contrast, SBSCs are precise but inaccurate and valuable for organizations desiring a comprehensive strategic management tool with limited capacity to handle sustainability issues. They pose a risk of the streetlight effect, where organisations do not measure relevant indicators but what is easy to measure.

Originality/value

The ideal CSP-measurement was identified as SPMSs, which are both precise and accurate. SPMSs are useful for organizations with complex, comprehensive, connected and tailored indicators but are methodologically challenging.

Details

Journal of Global Responsibility, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2041-2568

Keywords

Open Access
Article
Publication date: 22 August 2023

Mahesh Babu Purushothaman and Kasun Moolika Gedara

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and…

1429

Abstract

Purpose

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Design/methodology/approach

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Findings

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Research limitations/implications

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

Practical implications

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

Social implications

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Originality/value

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

Details

Smart and Sustainable Built Environment, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2046-6099

Keywords

Open Access
Article
Publication date: 24 May 2024

Bingzi Jin and Xiaojie Xu

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly…

Abstract

Purpose

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

Design/methodology/approach

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Findings

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Originality/value

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Details

Asian Journal of Economics and Banking, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2615-9821

Keywords

Open Access
Article
Publication date: 2 April 2024

Koraljka Golub, Osma Suominen, Ahmed Taiye Mohammed, Harriet Aagaard and Olof Osterman

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an…

Abstract

Purpose

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

Design/methodology/approach

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

Findings

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

Originality/value

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Details

Journal of Documentation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0022-0418

Keywords

Open Access
Article
Publication date: 13 May 2024

Lars Olbert

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a…

Abstract

Purpose

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

Design/methodology/approach

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

Findings

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

Originality/value

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

Details

Journal of Accounting Literature, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-4607

Keywords

Open Access
Article
Publication date: 26 April 2024

Xue Xin, Yuepeng Jiao, Yunfeng Zhang, Ming Liang and Zhanyong Yao

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic…

Abstract

Purpose

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

Design/methodology/approach

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

Findings

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

Originality/value

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Details

Smart and Resilient Transportation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2632-0487

Keywords

Open Access
Article
Publication date: 1 April 2021

Arunit Maity, P. Prakasam and Sarthak Bhargava

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is…

1338

Abstract

Purpose

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

Design/methodology/approach

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

Findings

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

Originality/value

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

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: 31 July 2023

Gennaro Maione, Corrado Cuccurullo and Aurelio Tommasetti

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for…

1652

Abstract

Purpose

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for managers and policymakers, whilst also outlining a future agenda for scholars.

Design/methodology/approach

A bibliometric analysis is carried out by adopting the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol for searching and selecting the scientific contributions to be analysed. Citation analysis is used to map a current research front and a bibliographic coupling is conducted to detect the connection networks in current literature.

Findings

Biodiversity accounting is articulated in five thematic clusters (sub-areas), such as “Natural resource management”, “Biodiversity economic evaluation”, “Natural capital accounting”, “Biodiversity accountability” and “Biodiversity disclosure and reporting”. Critical insights emerge from the content analysis of these sub-areas.

Practical implications

The analysis of the thematic evolution of the biodiversity accounting literature provides useful insights to inform both practice and research and infer implications for managers, policymakers and scholars by outlining three main areas of intervention, i.e. adjusting evaluation tools, integrating ecological knowledge and establishing corporate social legitimacy.

Social implications

Currently, the level of biodiversity reporting is pitifully low. Therefore, organisations should properly manage biodiversity by integrating diverse and sometimes competing forms of knowledge for the stable and resilient flow of ecosystem services for future generations.

Originality/value

This paper not only updates and enriches the current state of the art but also identifies five thematic areas of the biodiversity accounting literature for theoretical and practical considerations.

Details

Sustainability Accounting, Management and Policy Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8021

Keywords

Open Access
Article
Publication date: 13 November 2023

Rini Fitri, Reza Fauzi, Olivia Seanders and Dibyanti Danniswari

The purpose of the study is to analyze changes in land use, specifically residential area expansion, in South Tangerang City and identify the factors that influence land use…

Abstract

Purpose

The purpose of the study is to analyze changes in land use, specifically residential area expansion, in South Tangerang City and identify the factors that influence land use change.

Design/methodology/approach

The study used remote sensing methods in ArcGIS 10.8 for data analysis and processing, including spatial analysis and identification of land use changes. The study analyzed satellite images from 2010 and 2020 to identify changes in land use in South Tangerang City over the ten-year period.

Findings

The study found that the most significant land use changes in South Tangerang City between 2010 and 2020 were the reduction of mixed plantation area and the expansion of residential areas. The study identified the development of small townships by private developers as the main factor that influenced land use change in South Tangerang City.

Research limitations/implications

The study has several limitations, including a focus on only one aspect of land use change (i.e. residential area expansion), limited scope of the study area (South Tangerang City) and a reliance on remote sensing methods for data analysis.

Practical implications

The findings of the study can be used by policymakers and city planners to develop sustainable land use planning strategies that balance the need for urban development with environmental and social concerns. By understanding the factors that drive land use changes in South Tangerang City, policymakers can develop policies that encourage sustainable urban growth and development while preserving natural resources and protecting the environment.

Social implications

The study has social implications as the expansion of residential areas in South Tangerang City indicates a growing demand for housing in the area. The study highlights the importance of developing affordable and sustainable housing solutions to meet the needs of the growing population in South Tangerang City. Additionally, the study emphasizes the importance of understanding the social and economic factors that drive land use change and their implications for the well-being of local communities.

Originality/value

The residential area development in South Tangerang City is driven by private developers who make small independent cities that have all facilities in one area. These small cities attract people to reside and also drive high population growth in South Tangerang City, considering it is a buffer city of Jakarta that has good infrastructure development.

Details

Southeast Asia: A Multidisciplinary Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1819-5091

Keywords

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