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1 – 10 of over 1000
Article
Publication date: 9 February 2024

Ravinder Singh

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of…

Abstract

Purpose

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Design/methodology/approach

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

Findings

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

Originality/value

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Article
Publication date: 26 September 2023

Mohammed Ayoub Ledhem and Warda Moussaoui

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric…

Abstract

Purpose

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

Design/methodology/approach

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

Findings

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

Practical implications

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

Originality/value

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

Details

Journal of Modelling in Management, vol. 19 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 6 January 2023

Temidayo Oluwasola Osunsanmi, Timothy O. Olawumi, Andrew Smith, Suha Jaradat, Clinton Aigbavboa, John Aliu, Ayodeji Oke, Oluwaseyi Ajayi and Opeyemi Oyeyipo

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present…

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Abstract

Purpose

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

Design/methodology/approach

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

Findings

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Originality/value

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

Details

Property Management, vol. 42 no. 2
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 17 June 2021

Ambica Ghai, Pradeep Kumar and Samrat Gupta

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered…

1115

Abstract

Purpose

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

Design/methodology/approach

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

Findings

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

Research limitations/implications

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

Practical implications

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

Social implications

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

Originality/value

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 21 March 2024

Thamaraiselvan Natarajan, P. Pragha, Krantiraditya Dhalmahapatra and Deepak Ramanan Veera Raghavan

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and…

Abstract

Purpose

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

Design/methodology/approach

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

Findings

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Research limitations/implications

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

Social implications

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

Originality/value

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

Details

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

Keywords

Article
Publication date: 29 March 2024

Babajide Oyewo, Vincent Tawiah and Mohammad Alta’any

This study aims to investigate contextual factors affecting the deployment of strategy-driven manufacturing accounting techniques (SMAT), as well as the impact of SMAT usage on…

Abstract

Purpose

This study aims to investigate contextual factors affecting the deployment of strategy-driven manufacturing accounting techniques (SMAT), as well as the impact of SMAT usage on organisational competitiveness. Seven major SMAT were investigated, namely, benchmarking, integrated performance measurement, environmental management accounting, strategic costing, strategic pricing, strategic investment and life cycle costing.

Design/methodology/approach

By using multi-informant strategy, structured questionnaire was used to gather survey data from 129 senior accounting, finance and production personnel of publicly quoted manufacturing companies in Nigeria. Data was analysed using structural equation modelling and propensity score matching.

Findings

Result shows that the usage rate of the SMAT is generally moderate. Market orientation and deliberate strategy formulation are notable determinants of SMAT usage. The inability of competition intensity and perceived environmental uncertainty to notably affect SMAT usage suggests that external environmental pressure to use SMAT is weak.

Practical implications

Although the impact of SMAT usage on organisational competitiveness is positive and statistically significant, it is conceivable that the impact of SMAT could have been more assuming SMAT recorded extensive usage. Thus, the lack of competitiveness of manufacturing companies in Nigeria may not be unconnected to the superficial usage of SMAT.

Originality/value

The study contributes to knowledge in three ways. First, it extends studies on the contingency theory that contextual factors influence the adoption of management accounting innovations. Second, it exposes the contextual factors affecting the adoption of SMAT in a developing country. Third, it provides evidence on the value relevance of management accounting innovation in enhancing organisational competitiveness.

Details

Journal of Accounting & Organizational Change, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1832-5912

Keywords

Article
Publication date: 28 March 2024

Elisa Gonzalez Santacruz, David Romero, Julieta Noguez and Thorsten Wuest

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework…

Abstract

Purpose

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

Design/methodology/approach

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

Findings

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

Originality/value

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 19 March 2024

Cemalettin Akdoğan, Tolga Özer and Yüksel Oğuz

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of…

Abstract

Purpose

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Design/methodology/approach

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

Findings

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

Originality/value

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Details

Robotic Intelligence and Automation, vol. 44 no. 1
Type: Research Article
ISSN: 2754-6969

Keywords

Article
Publication date: 27 March 2024

Jyoti Mudkanna Gavhane and Reena Pagare

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Abstract

Purpose

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Design/methodology/approach

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Findings

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Originality/value

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Details

Education + Training, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0040-0912

Keywords

Open Access
Article
Publication date: 26 May 2023

Sina Moradi and Piia Sormunen

The construction industry has considerably evolved in the recent two decades due to the emergence of sustainability, lean construction (LC) and building information modelling…

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Abstract

Purpose

The construction industry has considerably evolved in the recent two decades due to the emergence of sustainability, lean construction (LC) and building information modelling (BIM). Despite previous research efforts, there is still a gap concerning the multidimensional nature of their integration. Hence, this study aims to fill the mentioned knowledge gap through exploring and comparing the challenges, enablers, techniques as well as benefits of integrating LC with BIM and sustainability in building construction projects.

Design/methodology/approach

A systematic literature review was conducted to fulfill the purpose of this study.

Findings

The findings reveal and compare the challenges, enablers, techniques and benefits of integrating LC with BIM and sustainability in building construction projects. The results suggest that there are eight common challenges for integrating LC with BIM and sustainability, including high initial cost, lack of collaboration, lack of professionals and lack of compatible contractual framework. The discovered challenges, enablers, techniques and benefits seem to be mostly routed in people. The findings also suggest that the synergistic benefits of integrating LC with BIM and sustainability can overcome the common challenges (safety, reliability, productivity, collaboration and quality) in construction projects.

Originality/value

The findings contribute to the literature and practice concerning the integration of LC with BIM and sustainability by exploring, comparing and discussing the relevant challenges, enablers, techniques as well as benefits. Moreover, the findings reveal the significance of the development of people in construction industry, besides processes and technology, as people are always subject of activities in construction while processes and technology are always objects.

Details

Construction Innovation , vol. 24 no. 7
Type: Research Article
ISSN: 1471-4175

Keywords

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