...Maja Borlinič Gačnik; Črtomir Rozman; Andrej Škraba Purpose This paper explores how the integration of machine learning (ML) and system dynamics (SD) can enhance understanding of climate change impacts on viticultural ecosystems, with a focus on vineyard biomass growth and wine quality under...

.... This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning. Design/methodology/approach Secondary data analysis was used in this study, which involved collecting information about 1,052 pre...

... and practical support for accident safety management. © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Construction safety accident Natural language processing Machine learning Tree-augmented naive Bayes Risk factor Jianhong Shen can be contacted...

... 2023 31 10 2023 01 01 2024 06 03 2024 07 03 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Machine learning Default prediction model Feature selection Logistic regression K-nearest neighbor Several ML...

..., in many models, minimizing the total cost for the organization has been overlooked. Design/methodology/approach This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different...

... algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated. Findings The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying...

... and interpretation methods. In recent years, the application of machine learning technology makes it possible for enterprises to accurately find weak signal signs in the competitive environment from a large number of data. As the amount of data continues to increase, it is necessary to constantly improve...

... machine learning methods, for CLV prediction. Design/methodology/approach In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared...

... of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms. Findings For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study...

... 23 03 2022 04 05 2022 08 06 2022 © Emerald Publishing Limited 2022 Emerald Publishing Limited Licensed re-use rights only Social media information Sentiment analysis Opinion mining Enterprise credit risk prediction Supply chain Machine learning Enterprise...

... in the random forest classifier for fraud detection (Lucas et al., 2020). Gianini et al. used a game theory-based approach for detecting credit card fraud by managing a pool of rules (Gianini et al., 2020). Machine learning Asexual reproduction optimization Credit card fraud...

... a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep...

... and machine learning algorithm. Primarily, items that affect the quality of service are determined based on the SERVQUAL scale. Subsequently, a service quality assessment model is generated to manage the resources that are allocated to improve the activities efficiently. Following this phase, a sample...

..., high school exam scores, region, CGPA) to allow for timely and efficient remediation. Design/methodology/approach A machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university. Findings Results revealed...

... over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which...

... for the mathematical modeling of this problem. Second-order cybernetics Machine learning Cybernetic modelling Non-trivial machine W. Ross Ashby W. Ross Ashby’s elementary non-trivial machine (NTM; Figure 1) is one of the most enigmatic of the artifacts created by the cybernetics movement in its...

... the similar features into clusters then eliminates unsatisfactory cluster. Seyed Mostafa Fakhrahmad can be contacted at: Mfakhrahmad@yahoo.com © Emerald Publishing Limited 2018 Emerald Publishing Limited Licensed re-use rights only Machine learning Sentiment analysis Feature clustering...

... popular machine learning classification algorithms including logistic regression, decision trees, support vector machines, neural networks and random forests. Findings A comparison of results shows that the proposed hybrid approach substantially outperforms the individual-level and the segment-based...

... applications to identify the most popular techniques. Chih-Fong Tsai is the corresponding author and can be contacted at: cftsai@mgt.ncu.edu.tw © Emerald Publishing Limited 2017 Emerald Publishing Limited Licensed re-use rights only Data mining Machine learning Survey Business...

...Chih-Fong Tsai; Chihli Hung Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving...