![]() ![]() IDC Spotlight: Looking Beyond the AI Model to Improve the Value of Computer Vision in Manufacturing Operations This IDC Spotlight explores the increasing adoption of computer vision (CV) by manufacturers, and how in order to succeed, they must focus on more than just model development, validation and deployment. How Do I Use SAS® Event Stream Processing With ONNX Models? Learn how SAS Event Stream Processing supports ONNX format models for inferencing using ONNX Runtime technology. Understanding data in motion Learn how to analyze fast-moving data streams on the fly with event stream processing.Ĭhanneling Streaming Data for Competitive Advantage Discover how and why innovative companies are transforming business operations by using streaming analytics to extract meaning from live data streams as data is created, and automate reactions to it with millisecond response times. And learn from the experiences of two companies that have successfully navigated organizational and technological challenges to adopt machine learning and embark on their own analytics evolution. The Evolution of Analytics Learn about modern applications for machine learning, including recommendation systems, streaming analytics, deep learning and cognitive computing. Learn how SAS Customer Intelligence 360 lets you use both cloud and on-site channels and data to create an omnichannel marketing solution. Using Hybrid Cloud Capabilities for True Omnichannel Marketing Seamless, agile customer interactions require a marketing system that can collect data about a customer’s interactions and behavior across all touch points, regardless of underlying technology. IoT data with artificial intelligence reduces downtime, helps truckers keep on trucking Volvo Trucks and Mack Trucks use sensor data and SAS AI solutions to minimize unplanned downtime. Transforming steelmaking through IoT analytics SSAB improves production efficiency, product quality and maintenance strategies using sensor data, artificial intelligence and advanced analytics. Managing Dutch roads and waterways with intelligence A modern AI, IoT and analytics platform powered by SAS Viya helps Rijkswaterstaat move from reactive to predictive infrastructure maintenance. Overall, the research highlights GBR-NMF as a valuable approach for Raman spectroscopy data deconstruction in radiation response monitoring, providing better biological interpretability compared to traditional methods like PCA.Analytics Accelerates Monetization Opportunities for Connected Vehicle and Mobility Services Learn how automakers and their partners are using IoT data and analytics to help them reshape business models, seize new sources of revenue and develop inventive ways to better serve customers. Some biochemical groups are more accurately deconstructed using GBR-NMF, likely due to similarities in their individual bases spectra. The inclusion of an unconstrained component has minimal impact on the deconstruction, as long as all biochemicals in the mixture are included as bases in the model. ![]() The study finds that solid-based spectra are comparable to solution-based spectra in the GBR-NMF model for all biochemical groups, and the model is relatively tolerant of high noise levels in mixture solutions with solid-based spectra. Factors such as solid versus solution-based spectra, the number of unconstrained components in the model, signal-to-noise thresholds, and comparisons between different groups of biochemicals are assessed. The authors compare GBR-NMF to more commonly used methods like principal component analysis (PCA) and evaluate the accuracy of GBR-NMF in reconstructing three mixture solutions with known concentrations. This article discusses the challenges and benefits of using group- and basis-restricted non-negative matrix factorization (GBR-NMF) as a dimensionality reduction technique for Raman spectroscopy data in the context of radiation response monitoring in biological samples. ![]()
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