• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 41
  • 13
  • 7
  • 5
  • 5
  • 3
  • 3
  • 2
  • 1
  • 1
  • 1
  • 1
  • Tagged with
  • 88
  • 88
  • 25
  • 20
  • 19
  • 18
  • 18
  • 16
  • 15
  • 13
  • 12
  • 11
  • 11
  • 10
  • 10
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Recommendation system for job coaches

Söderkvist, Nils January 2021 (has links)
For any unemployed person in Sweden that is looking for a job, the most common place they can turn to is the Swedish Public Employment Service, also known as Arbetsförmedlingen, where they can register to get help with the job search process. Occasionally, in order to land an employment, the person might require extra guidance and education, Arbetsförmedlingen outsource this education to external companies called providers where each person gets assigned a coach that can assist them in achieving an employment quicker. Given the current labour market data, can the data be used to help optimize and speed up the job search process? To try and help optimize the process, the labour market data was inserted into a graph database, using the database, a recommendation system was built which uses different methods to perform each recommendation. The recommendations can be used by a provider to assist them in assigning coaches to newly registered participants as well as recommending activities. The performance of each recommendation method was evaluated using a statistic measure. While the user-created methods had acceptable performance, the overall best performing recommendation method was collaborative filtering. However, there are definitely some potential for the user-created method, and given some additional testing and tuning, the methods can surely outperform the collaborative filtering method. In addition, expanding the database by adding more data would positively affect the recommendations as well.
32

Property Recommendation System with Geospatial Data Analytics and Natural Language Processing for Urban Land Use

Riehl, Sean K. 04 June 2020 (has links)
No description available.
33

Applying Deep Learning Techniques to Assist Bioinformatics Researchers in Analysis Pipeline Composition

Green, Ryan 02 June 2023 (has links)
No description available.
34

Recommendation System for Insurance Policies : An Investigation of Unsupervised and Supervised Learning Techniques

Palmgren, Andreas January 2023 (has links)
Recommendation systems have significantly influenced user experiences across various industries, yet their application in the insurance sector remains relatively unexplored. This thesis focuses on developing a car insurance recommendation system that implements a `consumers like you' feature. The study initially employs a clustering-based recommendation system due to missing labels in an offline environment. However, challenges emerge, such as determining the optimal number of clusters and managing complex data. Additionally, the inability to effectively update based on feedback and lower predictive performance compared to supervised methods necessitated exploring supervised alternatives. In response, this thesis proposes a methodology where the unsupervised approach simulates consumer behavior in an offline environment. Supervised alternatives are pre-trained on the clustering-based system to replicate it and come with the ability to be fine-tuned based on live traffic. Three supervised alternatives — KNN, XGBoost, and a neural network — are developed and compared. Given the supervised recommendation system adaptability based on feedback, supervised methods can provide more accurate, personalized recommendations in the insurance domain. The XGBoost and neural network-based recommendation systems were able to replicate the unsupervised approach, and their expressive power makes them valid candidate models to further evaluate on live traffic. The thesis concludes with the potential to both improve and adapt these recommendation systems to other insurance types, marking a significant step toward more personalized, user-friendly insurance services.
35

A Recommendation System Based on Multiple Databases.

Goyal, Vivek 11 October 2013 (has links)
No description available.
36

Sequential recommendation for food recipes with Variable Order Markov Chain / Sekventiell rekommendation för matrecept med Variable Order Markov Chain

Xu, Xuechun January 2018 (has links)
One of the key tasks in the study of the recommendation system is to model the dynamics aspect of a person's preference, i.e. to give sequential recommendations. Markov Chain (MC), which is famous for its capability of learning a transition graph, is the most popular approach to address the task. In previous work, the recommendation system attempts to model the short-term dynamics of the personal preference based on the long-term dynamics, which implies the assumption that the personal preference over a set of items remains same over time. However, in the field of food science, the study of Sensory-Specific Satiety (SSS) shows that the personal preference on food changes along time and previous meals. However, whether such changes follow certain patterns remains unclear. In this paper, a recommendation system is built based on Variable Order Markov Chain (VOMC), which is capable of modeling various lengths of sequential patterns using the suffix tree (ST) search. This recommendation system aims to understand and model the short-term dynamics aspect of the personal preference on food. To evaluate the system, a Food Diary survey is carried to collect users’ meals data over seven days. The results show that this recommendation system can give meaningful recommendations. / En av huvuduppgifterna när det kommer till rekommenderingsplatformar är att modellera kortsidiga dynamiska egenskaper, dvs. användares sekventiella beteenden. Markov Chain (MC), som är mest känd för sin förmåga att lära sig övergångsgrafer, är den mest populära metoden för att ge sig på denna uppgift. I föregående arbeten så har rekommenderingsplatformar ofta tenderat att modellera kortsidig dynamik baserat på långsidig dynamik, t.ex. likheter mellan objekt eller användares relativa preferenser givet olika tillfällen. Att använda den här metoden brukar medföra att användares långsiktiga dynamik, i detta fall personliga smakpreferenser, är alltid densamma. Däremot, så har studien av Sensory-Specific Satiety visat att användares preferenser gällande mat varierar. I detta arbete så undersöks ett rekommenderingssystem som baseras på Variable Order Markov Chain (VOMC) som kan anpassa sig efter den observerade realiseringen genom att använda suffix tree (ST) för att extrahera sekventiella mönster. Detta rekommenderingssystem fokuserar på kortsidig dynamik istället för att kombinera kort- och långsidig dynamik. För att evaluera metoden, en undersökning av vilken mat som konsumeras, under loppet av sju dagar, ges ut för att samla data om vilken mat och i vilken ordning användare konsumerar. I resultaten så visas att det föreslagna rekommenderingsystemet kan ge meningsfulla rekommendationer.
37

Sustainable Recipe Recommendation System: Evaluating the Performance of GPT Embeddings versus state-of-the-art systems

Bandaru, Jaya Shankar, Appili, Sai Keerthi January 2023 (has links)
Background: The demand for a sustainable lifestyle is increasing due to the need to tackle rapid climate change. One-third of carbon emissions come from the food industry; reducing emissions from this industry is crucial when fighting climate change. One of the ways to reduce carbon emissions from this industry is by helping consumers adopt sustainable eating habits by consuming eco-friendly food. To help consumers find eco-friendly recipes, we developed a sustainable recipe recommendation system that can recommend relevant and eco-friendly recipes to consumers using little information about their previous food consumption.  Objective: The main objective of this research is to identify (i) the appropriate recommendation algorithm suitable for a dataset that has few training and testing examples, and (ii) a technique to re-order the recommendation list such that a proper balance is maintained between relevance and carbon rating of the recipes. Method: We conducted an experiment to test the performance of a GPT embeddings-based recommendation system, Factorization Machines, and a version of a Graph Neural Network-based recommendation algorithm called PinSage for a different number of training examples and used ROC AUC value as our metric. After finding the best-performing model we experimented with different re-ordering techniques to find which technique provides the right balance between relevance and sustainability. Results: The results from the experiment show that the PinSage and Factorization Machines predict on average whether an item is relevant or not with 75% probability whereas GPT-embedding-based recommendation systems predict with only 55% probability. We also found the performance of PinSage and Factorization Machines improved as the training set size increased. For re-ordering, we found using a loga- rithmic combination of the relevance score and carbon rating of the recipe helped to reduce the average carbon rating of recommendations with a marginal reduction in the ROC AUC score.  Conclusion: The results show that the chosen state-of-the-art recommendation systems: PinSage and Factorization Machines outperform GPT-embedding-based recommendation systems by almost 1.4 times.
38

How Does Interface Design and Recommendation System in Video Streaming Services Affect User Experience? : A study on Netflix UI design and recommendation system and how it shapes the choices young adults between the ages 18 and 26 make.

Kindbom, Linnéa January 2022 (has links)
No description available.
39

Optimizing TEE Protection by Automatically Augmenting Requirements Specifications

Dhar, Siddharth 03 June 2020 (has links)
An increasing number of software systems must safeguard their confidential data and code, referred to as critical program information (CPI). Such safeguarding is commonly accomplished by isolating CPI in a trusted execution environment (TEE), with the isolated CPI becoming a trusted computing base (TCB). TEE protection incurs heavy performance costs, as TEE-based functionality is expensive to both invoke and execute. Despite these costs, projects that use TEEs tend to have unnecessarily large TCBs. As based on our analysis, developers often put code and data into TEE for convenience rather than protection reasons, thus not only compromising performance but also reducing the effectiveness of TEE protection. In order for TEEs to provide maximum benefits for protecting CPI, their usage must be systematically incorporated into the entire software engineering process, starting from Requirements Engineering. To address this problem, we present a novel approach that incorporates TEEs in the Requirements Engineering phase by using natural language processing (NLP) to classify those software requirements that are security critical and should be isolated in TEE. Our approach takes as input a requirements specification and outputs a list of annotated software requirements. The annotations recommend to the developer which corresponding features comprise CPI that should be protected in a TEE. Our evaluation results indicate that our approach identifies CPI with a high degree of accuracy to incorporate safeguarding CPI into Requirements Engineering. / Master of Science / An increasing number of software systems must safeguard their confidential data like passwords, payment information, personal details, etc. This confidential information is commonly protected using a Trusted Execution Environment (TEE), an isolated environment provided by either the existing processor or separate hardware that interacts with the operating system to secure sensitive data and code. Unfortunately, TEE protection incurs heavy performance costs, with TEEs being slower than modern processors and frequent communication between the system and the TEE incurring heavy performance overhead. We discovered that developers often put code and data into TEE for convenience rather than protection purposes, thus not only hurting performance but also reducing the effectiveness of TEE protection. By thoroughly examining a project's features in the Requirements Engineering phase, which defines the project's functionalities, developers would be able to understand which features handle confidential data. To that end, we present a novel approach that incorporates TEEs in the Requirements Engineering phase by means of Natural Language Processing (NLP) tools to categorize the project requirements that may warrant TEE protection. Our approach takes as input a project's requirements and outputs a list of categorized requirements defining which requirements are likely to make use of confidential information. Our evaluation results indicate that our approach performs this categorization with a high degree of accuracy to incorporate safeguarding the confidentiality related features in the Requirements Engineering phase.
40

Recommending TEE-based Functions Using a Deep Learning Model

Lim, Steven 14 September 2021 (has links)
Trusted execution environments (TEEs) are an emerging technology that provides a protected hardware environment for processing and storing sensitive information. By using TEEs, developers can bolster the security of software systems. However, incorporating TEE into existing software systems can be a costly and labor-intensive endeavor. Software maintenance—changing software after its initial release—is known to contribute the majority of the cost in the software development lifecycle. The first step of making use of a TEE requires that developers accurately identify which pieces of code would benefit from being protected in a TEE. For large code bases, this identification process can be quite tedious and time-consuming. To help reduce the software maintenance costs associated with introducing a TEE into existing software, this thesis introduces ML-TEE, a recommendation tool that uses a deep learning model to classify whether an input function handles sensitive information or sensitive code. By applying ML-TEE, developers can reduce the burden of manual code inspection and analysis. ML-TEE's model was trained and tested on functions from GitHub repositories that use Intel SGX and on an imbalanced dataset. The accuracy of the final model used in the recommendation system has an accuracy of 98.86% and an F1 score of 80.00%. In addition, we conducted a pilot study, in which participants were asked to identify functions that needed to be placed inside a TEE in a third-party project. The study found that on average, participants who had access to the recommendation system's output had a 4% higher accuracy and completed the task 21% faster. / Master of Science / Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.

Page generated in 0.1216 seconds