Essays about: "problems in performance"

Showing result 1 - 5 of 1540 essays containing the words problems in performance.

  1. 1. Station-level demand prediction in bike-sharing systems through machine learning and deep learning methods

    University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Author : Nikolaos Staikos; [2024]
    Keywords : Physical Geography; Ecosystem Analysis; Bike-sharing demand; Machine learning; Deep learning; Spatial regression; Graph Convolutional Neural Network; Multiple Linear Regression; Multilayer Perceptron Regressor; Support Vector Machine; Random Forest Regressor; Urban environment; Micro-mobility; Station planning; Geomatics; Earth and Environmental Sciences;

    Abstract : Public Bike-Sharing systems have been employed in many cities around the globe. Shared bikes are an efficient and convenient means of transportation in advanced societies. Nonetheless, station planning and local bike-sharing network effectiveness can be challenging. READ MORE

  2. 2. Zen Practice for the environment : A study of how Zen practice can help nurturing a healthy view of the environment

    University essay from Uppsala universitet/Teologiska institutionen

    Author : Josua Li; [2024]
    Keywords : Zen; Buddhism; Environment; Interconnections; Impermanence; Fishing; Meditation practices;

    Abstract : In this thesis, I explore how Zen can provide practices that help remedy the environmental problems that may arise from how the environment is viewed. I argue that two ingredients should be part of a good strategy to change how one views the environment. Firstly, a good strategy should not be in conflict with the worldview one holds. READ MORE

  3. 3. Time Series Forecasting on Database Storage

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

    Author : Pranav Patel; [2024]
    Keywords : Machine Learning; Time Series Forecasting; Prediction; Neural Networks; CNN; RNN; Database Storage;

    Abstract : Time Series Forecasting has become vital in various industries ranging from weather forecasting to business forecasting. There is a need to research database storage solutions for companies in order to optimize resource allocation, enhance decision making process and enable predictive data storage maintenance. READ MORE

  4. 4. Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods

    University essay from Lunds universitet/Statistiska institutionen

    Author : Markus Gerholm; Johan Sörstadius; [2024]
    Keywords : Linear regression; high dimensional data; regularization; Bayesian methods; Mathematics and Statistics;

    Abstract : As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. READ MORE

  5. 5. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

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

    Author : Khalid El Yaacoub; [2024]
    Keywords : Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

    Abstract : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. READ MORE