Sökning: "data driven AI decision-making"

Visar resultat 1 - 5 av 8 avhandlingar innehållade orden data driven AI decision-making.

1. 1. Data-driven AI Techniques for Fashion and Apparel Retailing

   Författare :Chandadevi Giri; Ulf Johansson; Jenny Balkow; Xianyi Zeng; Sebastien Thomessey; Maria Riveiro; Högskolan i Borås; []
   Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Digitalization; artificial intelligence; fashion and apparel industry; churn prediction; sales forecasting; campaign analysis; data driven AI decision-making; 数字化，人工智能，服装产业，客户流失预测，销售预测，竞争分析，数据驱动的人 工智能决策; Digitalisation; intelligence artificielle IA; industrie de la mode et de l habillement; prédiction de désabonnement; prévision des ventes; analyse des promotions; Prise de décision par IA axée sur les données; Digitalisering; Artificiell intelligens; Modeindustrin; Churnprediktion; Försäljningsprognoser; Kampanjanalys; Datadriven AI; Beslutsstöd; Business and IT; Handel och IT; Textil och mode generell ; Textiles and Fashion General ;

   Sammanfattning : Digitalisation allows companies to develop many new ways of interacting with customers and other stakeholders. These digital interactions typically generate data that can be stored and later processed for different objectives. LÄS MER

3. 2. Data management and Data Pipelines: An empirical investigation in the embedded systems domain

   Författare :Aiswarya Raj Munappy; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; data management; empirical investigation; artificial intelligence; data pipelines; embedded systems; software engineering; machine learning;

   Sammanfattning : Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. LÄS MER

4. 3. Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health

   Författare :Alexander Galozy; Sławomir Nowaczyk; Mattias Ohlsson; Fredrik Johansson; Högskolan i Halmstad; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Information Driven Care; Electronic Health Records; Machine Learning; Reinforcement Learning;

   Sammanfattning : Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. LÄS MER

5. 4. Data Driven AI Assisted Green Network Design and Management

   Författare :Meysam Masoudi; Cicek Cavdar; Jens Zander; Muhammad Ali Imran; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; 6G; 5G; Energy efficiency; Machine learning; Reinforcement learning; Network architecture; Sleep modes; Mobile networks; 5G; C-RAN; nätverksarkitektur; nätverksdelning; maskininlärning;

   Sammanfattning : The energy consumption of mobile networks is increasing due to an increase in traffic demands and the number of connected users to the network. To assure the sustainability of mobile networks, energy efficiency must be a key design pillar of the next generations of mobile networks. LÄS MER

7. 5. Designing with Machine Learning in Digital Pathology : Augmenting Medical Specialists through Interaction Design

   Författare :Martin Lindvall; Jonas Löwgren; Claes Lundström; Darren Treanor; Andreas Holzinger; Linköpings universitet; []
   Nyckelord :HUMANIORA; HUMANITIES; NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : Recent advancements in machine learning (ML) have led to a dramatic increase in AI capabilities for medical diagnostic tasks. Despite technical advances, developers of predictive AI models struggle to integrate their work into routine clinical workflows. LÄS MER