PySS3: A Python package implementing SS3 text classifier with visualizations tools for explainable artificial intelligence (XAI) Lpproj: A Python implementation of Locality Preserving Projections (LPP) with Scikit-Learn compatible API Multi-Rake: Multilingual rapid automatic keyword extraction (Multi-RAKE...
AI security isn’t just about preventing attacks – it’s also about ensuring your AI systems are transparent, explainable, and free from bias. Establish ethical guidelines for AI use and data privacy. Engage with policymakers and stakeholders to contribute to the development of responsible AI prac...
To this end, this tutorial aims to introduce the basic principles for implementing AI and explainable AI (X-AI) algorithms in image-based data analysis (Figure 1) using the PlantVillage dataset as a case study to accurately identify and classify tomato leaf diseases and spider mites, as well...
Thankfully, there’s momentum to change that with investments in Explainable AI, wherein the model would be able to not just provide predictions but also account for the factors that caused it to make a certain prediction, and reveal areas of limitations. Additionally, cities (such as New ...
Explainable AI (XAI) and decision-making models for computational sustainability Sustainable development using edge computing, fog computing and cloud computing Cognitive intelligent systems for e-learning Artificial Intelligence and machine learning for large scale data ...
Description Get quick hands-on experience with Google Cloud. This cookbook provides a variety of self-contained recipes that show you how to use Google Cloud services for your enterprise application. Whether you’re looking for practical ways to apply microservices, AI, analytics, security, or netw...
Transparency: Ensure the vendor provides explainable AI features for clarity in decision-making. Ethical Standards: Confirm alignment with ethical guidelines like AI responsibility and fairness. 10. Pilot and Scale Trial Phase: Run a pilot project to evaluate the product’s real-world effectiveness and...
Machine Learning in Materials Science: From Explainable Predictions to Autonomous Design. Comput. Mater. Sci. 2021, 193, 110360. [Google Scholar] [CrossRef] Menezes, B.C.; Kelly, J.D.; Leal, A.G. Identification and Design of Industry 4.0 Opportunities in Manufacturing: Examples from Mature ...