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Showing result 1 - 5 of 2067 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. Optical Communication using Nanowires and Molecular Memory Systems

   University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/Synkrotronljusfysik

   Author : Thomas Kjellberg Jensen; [2024]
   Keywords : neuromorphic computing; nanowire; molecular dye; DASA photoswitch; OBIC; Physics and Astronomy;

   Abstract : Neuromorphic computational networks, inspired by biological neural networks, provide a possible way of lowering computational energy cost, while at the same time allowing for much more sophisticated devices capable of real-time inferences and learning. Since simulating artificial neural networks on conventional computers is particularly inefficient, the development of neuromorphic devices is strongly motivated as the reliance on AI-models increases. READ MORE

4. 3. 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

5. 4. Privatträdgårdens potential som resurs för gynnande av pollinationssamhällen

   University essay from SLU/Dept. of Landscape Architecture, Planning and Management (from 130101)

   Author : Karin Ericsson; [2024]
   Keywords : Pollinatörer; Biologisk mångfald; Urbanisering; Gynnande åtgärder; Påverkansfaktorer;

   Abstract : I detta arbete undersöks hotbilden mot de viktigaste pollinatörerna i Sverige, påverkansfaktorer samt vilka handlingsalternativ som finns för privatpersoner med engagemang i frågan. I dagsläget är många viktiga pollinatörer globalt och nationellt hotade, och därmed också de ekosystemtjänster som vi människor drar nytta av dagligen. READ MORE

6. 5. Control system and simplified timesynchronization for heterogenous IoT systems with medium time requirements

   University essay from KTH/Hälsoinformatik och logistik

   Author : Jemma Touma; Simon Hejdenberg; [2024]
   Keywords : Internet of Things; time synchronization; wireless sensor networks; network time protocol; Android; Bluetooth; WiFi Direct;

   Abstract : The company QTPIE conducts research on drivers and their unconscious reactions when driving. To help, they use smart devices that today must be individually handled at the start and end of a run, and have individually set timestamps, which can lead to differences between the units when data is entered and collation of the units' data after a run. READ MORE