Results 61 to 70 of about 15,135,820 (245)
Advanced Engineering Materials, EarlyView.Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios +5 more
wiley +1 more source
Negative Transfer in Cross Project Defect Prediction: Effect of Domain Divergence
, 2022 Experimental results from the paper titled "Negative Transfer in Cross Project Defect Prediction: Effect of Domain ...
Sherlock A Licorish +2 more
core +1 more source
Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows
Advanced Engineering Materials, EarlyView.A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba +5 more
wiley +1 more source
IET Software, 2021 Due to the differentiation between training and testing data in the feature space, cross‐project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot‐spot for
Quanyi Zou +4 more
doaj +1 more source
An Empirical Study of Classifier Combination on Cross-Project Defect Prediction [PDF]
, 2015 —To help developers better allocate testing and de-bugging efforts, many software defect prediction techniques have been proposed in the literature. These techniques can be used to predict classes that are more likely to be buggy based on past history of
David LO, +7 more
core +1 more source
A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data
Advanced Engineering Materials, EarlyView.Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour +5 more
wiley +1 more source
Cross Project Defect Prediction Menggunakan Random Forest [PDF]
This study develops a software defect prediction model using the Random Forest algorithm with a Many-to-One Cross-Project Defect Prediction approach. The model is tested using the AEEEM dataset as the training data source and the PROMISE dataset as the ...
Darusman, Darusman, Subekti, Agus
core +1 more source
Cross-Project Software Defect Prediction Based on Domain Adaptation and Feature Fusion
AlgorithmsWith the advancement of computer science, software has become increasingly prevalent across all facets of society, making software quality issues a focal point of industry concern.
Guanhua Guo, Yinglei Song, Peng Zhang
doaj +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Research and Appalication of Software Defect Predictionn based on BP-Migration learning
MATEC Web of Conferences, 2018 Software Defect Prediction has been an important part of Software engineering research since the 1970s. This technique is used to calculate and analyze the measurement and defect information of the historical software module to complete the defect ...
Zhang Jie +4 more
doaj +1 more source