Sökning: "prediction"

Visar resultat 6 - 10 av 3141 uppsatser innehållade ordet prediction.

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

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Nikolaos Staikos; [2024]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

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

   Kandidat-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

   Författare :Abel Getachew Kenea; Gabriel Fagerslett; [2024]
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

4. 8. Assessing water balance and yields in Malawian cropping systems : maize soybean and maize Gliricidia systems resilience against climate change

   Master-uppsats, SLU/Dept. of Soil and Environment

   Författare :Danila Valeriano; [2024]
   Nyckelord :crop modelling; APSIM; Malawi; maize; soybean; Gliricidia; agroforestry; legume intercropping; climate predictions; climate adaptation; SDSM;

   Sammanfattning : In Malawi, maize monocultures are increasingly susceptible to extreme weather patterns, causing considerable yield reduction and heightened food insecurity for smallholder farmers dependent on rainfed subsistence agriculture. Diversifying cropping systems is crucial for ensuring yield resilience. LÄS MER

5. 9. Learning a Grasp Prediction Model for Forestry Applications

   Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för fysik

   Författare :Elias Olofsson; [2024]
   Nyckelord :Forwarder; Autonomous grasping; Deep learning; Multibody dynamics; Convolutional neural network;

   Sammanfattning : Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. LÄS MER

7. 10. Predicting Electricity Consumption with ARIMA and Recurrent Neural Networks

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Klara Enerud; [2024]
   Nyckelord :time series forecasting; ARIMA; recurrent neural networks; LSTM; electricity forecasting; EED forecasting;

   Sammanfattning : Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). LÄS MER