Essays about: "Trade Show Intelligence"

Showing result 1 - 5 of 11 essays containing the words Trade Show Intelligence.

  1. 1. Counterfactual and Causal Analysis for AI-based Modulation and Coding Scheme Selection

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Kun Hao; [2023]
    Keywords : Explainable Artificial Intelligence; Counterfactual; Causal Analysis; Shapley Additive Explanations; Modulation and Coding Scheme; Förklarlig artificiell intelligens; kontrafaktisk analys; orsaksanalys; Shapley tillsatsförklaringar; modulerings och kodningsschema;

    Abstract : Artificial Intelligence (AI) has emerged as a transformative force in wireless communications, driving innovation to address the complex challenges faced by communication systems. In this context, the optimization of limited radio resources plays a crucial role, and one important aspect is the Modulation and Coding Scheme (MCS) selection. READ MORE

  3. 2. Ray-tracing Based Investigations on the Deployment of RISs in Indoor Scenarios

    University essay from Lunds universitet/Institutionen för elektro- och informationsteknik

    Author : Swaroop Divya Sai Prem Nambala; Dexin Kong; [2023]
    Keywords : Reconfigurable intelligent surface; RIS; optimization; Ray-tracing; Mixed integer linear programming MILP ; Complex impulse response; Wireless Insite.; Technology and Engineering;

    Abstract : Reconfigurable intelligence surface (RIS) is a promising candidate technology for future 6G wireless communication systems. In existing communication systems, the network operators are unable to control the propagation environment, which causes significant limitations on communication performance. READ MORE

  4. 3. Image generation through feature extraction and learning using a deep learning approach

    University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Author : Tibo Bruneel; [2023]
    Keywords : Deep Learning; Neural Networks; Deep Generative Learning; Variational Autoencoders; Generative Adversarial Networks; Flow-based Models; Triplet Image Generation; Triplet Loss; Tree Log End Generation; Forestry Application;

    Abstract : With recent advancements, image generation has become more and more possible with the introduction of stronger generative artificial intelligence (AI) models. The idea and ability of generating non-existing images that highly resemble real world images is interesting for many use cases. READ MORE

  5. 4. Peeking Through the Leaves : Improving Default Estimation with Machine Learning : A transparent approach using tree-based models

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Elias Hadad; Angus Wigton; [2023]
    Keywords : Machine learning; Expected credit loss; Probability of default; ECL; PD; Risk Management; Credit Risk Management; Default Estimation; AI; Artificial intelligence; Fintech; Supervised learning; Decision tree; Random forest; XG boost; Transparency; Machine learning transparency;

    Abstract : In recent years the development and implementation of AI and machine learning models has increased dramatically. The availability of quality data paving the way for sophisticated AI models. Financial institutions uses many models in their daily operations. READ MORE

  6. 5. Explainable Machine Learning for Lead Time Prediction : A Case Study on Explainability Methods and Benefits in the Pharmaceutical Industry

    University essay from KTH/Hållbar produktionsutveckling (ML)

    Author : Paul Fussenegger; Niklas Lange; [2022]
    Keywords : Lead time; machine learning; explainability; regression analysis; production planning and control; Ledtid; maskininlärning; förklarbarhet; regressionsanalys; produktionsplanering och produktionsstyrning;

    Abstract : Artificial Intelligence (AI) has proven to be highly suitable for a wide range of problems in manufacturing environments, including the prediction of lead times. Most of these solutions are based on ”black-box” algorithms, which hinder practitioners to understand the prediction process. READ MORE