... with Machine Learning and Deep Learning algorithms used for new business model developments, R&D purposes, and for better utilization of the resources available. Table 1 Applications of data science tools across business verticals Business verticals/Industries Data science tools...

... system or collusion, and (3) attitude/rationalization, which is the ability of a perpetrator to justify the fraud to overcome feelings of guilt, as illustrated in Figure 2 . The integration of artificial intelligence (AI) and machine learning (ML) into auditing has created new...

... decision-making, facilitate knowledge externalization, and promote collaboration (Aier et al., 2011). The emergence of artificial intelligence (AI) is accelerating this convergence. AI technologies, including machine learning, natural language processing (NLP), semantic reasoning, and large...

.... Design/methodology/approach The methodology involves analyzing the proximity of rental stations to significant locations, historical demand, environmental factors and regional dynamics to inform the development of AI models. Various machine learning (ML) models were evaluated to identify an optimized...

... Kumar Singh can be contacted at: rohit.singh696@gmail.com 28 03 2023 03 07 2023 13 11 2023 20 02 2024 24 05 2024 15 01 2025 11 02 2025 14 02 2025 AHP Artificial intelligence Big data analytics DEMATEL FMCG Machine learning Organization information...

...Oscar F. Bustinza; Luis M. Molina Fernandez; Marlene Mendoza Macías Purpose Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product...

... consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose. Findings SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving...

... from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). Design/methodology/approach In the present study, the authors use...

... After comparing two machine learning algorithms, we found out that self-organizing map better classifies the customer base of the retailer. The algorithm was able to extract three clusters that were described as personas using the values of the customer lifetime value and the scores of the variant...

... modeling to personnel decision-making using the design science paradigm and guided by CAM as the kernel theory. The study employed machine learning techniques, producing a model that predicts the probability of free agents signing a new contract. Also, a web-based tool was developed to help decision-makers...

...-Pereira et al., 2020). Marzia Hoque Tania can be contacted at: marzia.hoquetania@eng.ox.ac.uk 31 01 2020 15 05 2020 12 07 2020 © Emerald Publishing Limited 2020 Emerald Publishing Limited Licensed re-use rights only Computer vision Machine learning...

... a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance. Findings This study shows that the success of crowdfunding...

... decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning. Design/methodology/approach This framework focuses on the automatic annotation and metadata completion through new deep...

...Murat Özemre; Ozgur Kabadurmus Purpose The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology. Design/methodology/approach In this study, two different machine learning algorithms, Random Forest (RF) and Artificial...