Researchers have optimized a headspace sorptive extraction (HSSE) method coupled with gas chromatography-mass spectrometry ...
Soft Computing (SC) is an Artificial Intelligence (AI) approach that is more effective at solving real-life problems than traditional computing models. Soft Computing models are tolerant of partial ...
Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational ...
An algorithm that finds lost civilizations is helping archaeologists use AI to predict where ancient sites may still be ...
Objective To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician ...
Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...
Abstract: Reducing energy consumption has become a pressing need for modern machine learning, which has achieved many of its most impressive results by scaling to larger and more energy-consumptive ...
Abstract: With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next ...
An image depicting the integration of AI technologies in banking, showcasing how legacy banks can evolve with AI advancements for improved customer experiences and operational efficiency. Supervised ...
The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...
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