Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences

MZ Naser, AH Alavi - Architecture, Structures and Construction, 2023 - Springer
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …

A survey of predictive modeling on imbalanced domains

P Branco, L Torgo, RP Ribeiro - ACM computing surveys (CSUR), 2016 - dl.acm.org
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Multi-objective hyperparameter optimization in machine learning—An overview

F Karl, T Pielok, J Moosbauer, F Pfisterer… - ACM Transactions on …, 2023 - dl.acm.org
Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …

A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil

N Kardani, A Bardhan, P Samui, M Nazem… - Engineering with …, 2022 - Springer
Thermal conductivity is a specific thermal property of soil which controls the exchange of
thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect …

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

MF Javed, MZ Shahab, U Asif, T Najeh, F Aslam… - Scientific Reports, 2024 - nature.com
The escalation of global urbanization and industrial expansion has resulted in an increase
in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of …

Modeling wine preferences by data mining from physicochemical properties

P Cortez, A Cerdeira, F Almeida, T Matos… - Decision support systems, 2009 - Elsevier
We propose a data mining approach to predict human wine taste preferences that is based
on easily available analytical tests at the certification step. A large dataset (when compared …

Contrastive context-aware learning for 3d high-fidelity mask face presentation attack detection

A Liu, C Zhao, Z Yu, J Wan, A Su, X Liu… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Face presentation attack detection (PAD) is essential to secure face recognition systems
primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from …

Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

S Sathvik, R Kumar, N Ulloa, P Shakor, MS Ujwal… - Scientific Reports, 2024 - nature.com
India's cement industry is the second largest in the world, generating 6.9% of the global
cement output. Polycarbonate waste ash is a major problem in India and around the globe …

GPTIPS 2: an open-source software platform for symbolic data mining

DP Searson - Handbook of genetic programming applications, 2015 - Springer
Genetic programming (GP; Koza 1992) is a biologically inspired machine learning method
that evolves computer programs to perform a task. It does this by randomly generating a …

Evaluating prediction systems in software project estimation

M Shepperd, S MacDonell - Information and Software Technology, 2012 - Elsevier
CONTEXT: Software engineering has a problem in that when we empirically evaluate
competing prediction systems we obtain conflicting results. OBJECTIVE: To reduce the …