Essays about: "cluster development"

Showing result 1 - 5 of 229 essays containing the words cluster development.

  1. 1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Author : Alexander Florean; [2024]
    Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

    Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE

  3. 2. Virtual Sensing for Cluster Vacuum Control

    University essay from KTH/Skolan för industriell teknik och management (ITM)

    Author : Carl Egenäs; Axel Sacilotto; [2023]
    Keywords : ;

    Abstract : The purpose of this project was to develop a virtual sensing model in order to estimate the vacuum level in the milking cluster with data collected further along the milk tube. Measuring the cluster vacuum level is desirable for many applications, such as control and monitoring, but due to the cow's tendency to kick, chew and stomp on anything within reach, placing a sensor in or close to the cluster is both difficult and impractical. READ MORE

  4. 3. Exploring Greenhouse Gas Emissions and socio-economic factors for climate change mitigation: A worldwide clustering analysis

    University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

    Author : Anna Pasini; [2023]
    Keywords : GHG emissions; Climate Change; Panel Data; Gaussian Mixture Models Cluster Analysis; PCA; Business and Economics;

    Abstract : In response to the pressing need to address climate change and reduce global greenhouse gas (GHG) emissions, this study implemented Gaussian Mixture Models Clustering to detect the levels of GHG emissions and related socio-economic factors in 174 countries. To handle the panel data, Principal Component Analysis was conducted to achieve dimension reduction. READ MORE

  5. 4. Finding economic feasibility in electrified LTL transportation systems

    University essay from Lunds universitet/Produktionsekonomi

    Author : Eddi Johansson; Tusjant Ruthran; [2023]
    Keywords : Electric Trucks; Electrified LTL; Electrified Less-than-Truckload; Electrified LTL Transportation System; Electrified Less-than-Truckload Transportation System; Economics Electrified LTL.; Technology and Engineering;

    Abstract : As electrification of vehicles moves past its infancy and large-scale adoption of the technology becomes reality, application areas within different industries need to be scrutinized. The shift within the transportation sector, especially trucking, can have unidentified economic benefits; which is the focus of this thesis. READ MORE

  6. 5. Exploring Advanced Clustering Techniques for Business Descriptions : A Comparative Study and Analysis of DBSCAN, K-Means, and Hierarchical Clustering

    University essay from Mittuniversitetet/Institutionen för data- och elektroteknik (2023-)

    Author : Wisam Orabi Alkhen; [2023]
    Keywords : Machine learning; Business descriptions; Search scope reduction; Relevant business terminology; Data analysis.;

    Abstract : In this study, we introduce several approaches to analyze large volumes of business descriptions by applying machine learning clustering and classification algorithms. The goal is to efficiently classify these descriptions, reducing the search scope and allowing for better business insights and decision-making processes. READ MORE