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Design Pattern Detection in Code
Get PDFSoftware design patterns and the abstractions they offer can support developers and maintainers with program code comprehension. Yet manually-created pattern documentation within code or code-related assets, such as documents or models, can be unreliable, incomplete, and labor-intensive. While various Design Pattern Detection (DPD) techniques have been proposed, industrial adoption of automated DPD remains limited. This paper contributes a hybrid DPD solution approach that leverages a Bayesian network integrating developer expertise via rule-based micropatterns with our machine learning subsystem that utilizes graph embeddings. The prototype shows its feasibility, and the evaluation using three design patterns shows its potential for detecting both design patterns and variations
An interlaboratory capillary zone electrophoresis‐UV study of various monoclonal antibodies, instruments, and ε‐aminocaproic acid lots
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Design and prototype development of an AI-based application for predictive maintenance using smart device and airborne noise data
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