...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...

.... Existing studies have not fully mined the information from accident reports. With the development of natural language processing (NLP), machine learning (ML) and deep learning (DL), emerging technologies provide technical support for challenges in text utilization. These technologies have demonstrated some...

... Licensed re-use rights only Machine learning Default prediction model Feature selection Logistic regression K-nearest neighbor Credit risk assessment is attracting a higher level of attention than ever before because of the increase in financial innovation, financial inclusions and rapid...

..., 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...

... recognition Weak signal evolution Enterprise foresight Machine learning Domain ontology Extension theory The intensification of competition and the requirements of innovation make strategic management more complex, and enterprises urgently need to grope ahead in a dynamically changing market...

... 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...

.... Social media information Sentiment analysis Opinion mining Enterprise credit risk prediction Supply chain Machine learning Enterprise credit risk assessment and prediction is one of the key issues in the field of financial risk and corporate finance (Wang and Ku, 2021). Since the global...

... that using the proposed method enables us to achieve reasonable accuracy faster, compared to one of the state-of-the-art fraud detection methods, i.e. artificial immune systems (AIS). Machine learning Asexual reproduction optimization Credit card fraud detection Fraud detection Artificial immune...

... 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...

... 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 transdisciplinary quest for a systematic understanding of life, mind...

.... modeled aclMDb data set in 2016 using a set of unigram and bigram features. Authors classified the created models with machine learning algorithms (Tripathy et al., 2016). The presented method consists of three steps which are briefly listed below: Pre-processing was initially carried out...

... 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...

.... (2013) ARIMA [ 23 ] GA LR MLP MOEA [ 24 ] Other financial time-series areas Ahmed et al. (2010) BN [ 25 ] CART GRNN [ 26 ] K-NN MLP RBFNN SVR [ 27 ] From the machine learning viewpoint, these techniques can be classified into supervised and unsupervised learning...

...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...