A review of the use of artificial intelligence methods in infrastructure systems

Bibliometric Analysis of the Application of Artificial Intelligence Techniques to the Management of Innovation Projects

“…The construction sector is usually linked to information and knowledge management for product development and sustainability. Other recent research [65][66][67][68] focusing on the impact of artificial intelligence in the construction sector reached similar conclusions, with a continued increase in the number of scientific investigations in this field.…”

Assisting artificial intelligence adoption drivers in human resources management: a mediation model

“…In the context of a corporate IT environment, infrastructure refers to the collective hardware, software, network resources, and services that are necessary for the existence, operation, and management of the environment [43][44][45]. Infrastructure systems will be crucial in supplying and maintaining services for this ever-increasing demand as AI applications become more connected, complicated, and digitalized [46]. [47] determined that without the critical infrastructure in an organization and without a high level of readiness, AI applications will fail.…”

Development of local and global wastewater biochemical oxygen demand real-time prediction models using supervised machine learning algorithms

“…Recursive construction of simple decision binary trees is done by an exhaustive search for potential node split candidates of each feature (Bishop, 2006). The iterative process is done to nominate a node-splitting rule that gives a prediction 𝑦 𝑚 (𝑥) making the error 𝐸(𝑥) reach a certain threshold or when the node contains samples less than a splitting requirement (McMillan et al, 2022). The error was calculated using Eq.…”

“…The random forest (RF) algorithm is based on assembling a group of trained decision trees (McMillan et al, 2022). The decision trees were trained on slightly different portions of the dataset to predict the numerical values using the bootstrapping technique (Mehrani et al, 2022) (Torregrossa et al, 2018).…”

“…Unlike legacy models, it has great potential to explore a new understanding of wastewater behavior (Xie et al, 2022;Wang et al, 2021;, which is challenging to observe. Machine-learning integration with wastewater treatment processes will be a guidance tool to improve the treatment process (McMillan et al, 2022), reduce energy consumption and operational costs (Boyd et al, 2019), optimize plant performance (Icke et al, 2020), and enhance environmental preservation. Azimi et al (2022) proposed three separate optimized ANN using genetic algorithm (GA) models to predict WWTP effluent COD, BOD5, and TSS.…”