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Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
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 …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
A survey of predictive modeling on imbalanced domains
Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …
Multi-objective hyperparameter optimization in machine learning—An overview
Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …
(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
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 …
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
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 …
in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of …
Modeling wine preferences by data mining from physicochemical properties
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 …
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
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 …
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
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 …
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 …
that evolves computer programs to perform a task. It does this by randomly generating a …
Evaluating prediction systems in software project estimation
CONTEXT: Software engineering has a problem in that when we empirically evaluate
competing prediction systems we obtain conflicting results. OBJECTIVE: To reduce the …
competing prediction systems we obtain conflicting results. OBJECTIVE: To reduce the …