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Showing result 1 - 5 of 1266 essays matching the above criteria.

1. 1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience

   University essay from KTH/Hälsoinformatik och logistik

   Author : Habib Jabeli; [2024]
   Keywords : Machine Learning; Flight Comparison; Flygresor.se; Neural Networks; Flight Ranking; Random Forest; XGBoost;

   Abstract : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. READ MORE

3. 2. ML implementation for analyzing and estimating product prices

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

   Author : Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Keywords : Machine Learning; ML; Regression; Deep Learning; Artificial Neural Network; ANN; TensorFlow; ScikitLearn; CUDA; cuDNN; Estimation; Prediction; AI; Artificial Intelligence; Price Tracking; Price Logging; Price Estimation; Supervised Learning; Random Forest; Decision Trees; Batch Learning; Hyperparameter Tuning; Linear Regression; Multiple Linear Regression; Maskininlärning; Djup lärning; Artificiellt Neuralt Nätverk; Regression; TensorFlow; SciktLearn; ML; ANN; Estimation; Uppskattning; CUDA; cuDNN; AI; Artificiell Intelligens; pris loggning; pris estimation; prisspårning; Batchinlärning; Hyperparameterjustering; Linjär Regression; Multipel Linjär Regression; Supervised Learning; Random Forest; Decision Trees;

   Abstract : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. READ MORE

4. 3. Data analytics and machine learning for railway track degradation: Using Bothnia Line track measurements for maintenance forecasting

   University essay from KTH/Väg- och spårfordon samt konceptuell fordonsdesign

   Author : Elie Roudiere; [2024]
   Keywords : Railway; Track geometry; Machine learning; Statistics; Predictive maintenance; Botniabanan; Järnväg; spårgeometri; maskininlärning; statistik; förebyggande underhåll; Botniabanan;

   Abstract : In this paper, a statistical method is developed to improve predictive maintenance on railway track. The problem tackled is being able to predict when the next maintenance event should take place to guarantee a certain track quality class. READ MORE

5. 4. 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

6. 5. Implementing End-to-End MLOps for Enhanced Steel Production

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

   Author : Marcus Westin; Jacob Berggren; [2024]
   Keywords : MLOps; Azure ML; Machine Learning; Computer Science; Microsoft Azure; MLOps; Azure ML; Maskininlärning; Datavetenskap; Microsoft Azure;

   Abstract : Steel production companies must utilize new technologies and innovations to stay ahead of a highly competitive market. Recently, there has been a focus on Industry 4.0, which involves the digitalization of production to integrate with newer technologies such as cloud solutions and the Internet of Things (IoT). READ MORE