首页|Singidunum University Researcher Has Provided New Data on Machine Learning (Expl oring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets)
Singidunum University Researcher Has Provided New Data on Machine Learning (Expl oring Metaheuristic Optimized Machine Learning for Software Defect Detection on Natural Language and Classical Datasets)
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Investigators publish new report on ar tificial intelligence. According to news reporting from Belgrade, Serbia, by New sRx journalists, research stated, "Software is increasingly vital, with automate d systems regulating critical functions. As development demands grow, manual cod e review becomes more challenging, often making testing more time-consuming than development." Funders for this research include Science Fund of The Republic of Serbia; Charac terizing Crises-caused Air Pollution Alternations Using An Artificial Intelligen ce-based Framework. The news correspondents obtained a quote from the research from Singidunum Unive rsity: "A promising approach to improving defect detection at the source code le vel is the use of artificial intelligence combined with natural language process ing (NLP). Source code analysis, leveraging machine-readable instructions, is an effective method for enhancing defect detection and error prevention. This work explores source code analysis through NLP and machine learning, comparing class ical and emerging error detection methods. To optimize classifier performance, m etaheuristic optimizers are used, and algorithm modifications are introduced to meet the study's specific needs. The proposed two-tier framework uses a convolut ional neural network (CNN) in the first layer to handle large feature spaces, wi th AdaBoost and XGBoost classifiers in the second layer to improve error identif ication. Additional experiments using term frequency-inverse document frequency (TF-IDF) encoding in the second layer demonstrate the framework's versatility."
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