Search
Now showing items 1-10 of 10
AI Safe Exploration: Reinforced learning with a blocker in unsafe environments
(2019-11-12)
Artificial intelligence can be trained with a trial and
error based approach. In an environment where a catastrophe
can not be accepted a human overseer can be used, but this
might lower the efficiency of the learning. ...
Fault Prediction in Android Systems through AI
(2019-10-04)
Software code defect prediction is important in improving code quality and the turnaround time of software products. In this thesis we investigate how to create and extract features, analyze existing work to create and ...
An approach to automate accident scenario generation using recurrent neural networks
(2019-11-26)
There is a need to improve the test procedure of Active Safety Systems through the automation of scenario generation, especially accident scenarios that are critical for testing. The purpose of this thesis is to provide ...
APPLYING MACHINE LEARNING ALGORITHMS TO DETECT LINES OF CODE CONTRIBUTING TO TECHNICAL DEBT
(2019-11-12)
This paper shows the investigation of the viability of finding lines of
code (LOC) contributing to technical debt (TD) using machine learning (ML), by
trying to imitate the static code analysis tool SonarQube. This is ...
Machine Learning for Suicide Risk Assessment on Depressed Patients
(2019-11-21)
Suicide is a global phenomena and the leading cause of death in some countries
and age groups, accounting for nearly 1 million deaths and 10 million attempts per
year. It costs society a substantial amount of money both ...
Investigating the Accuracy of Metric-Based versus Machine Learning Approaches in Detecting Design Patterns
(2023-08-03)
Design pattern detection approaches have evolved, with machine-learning methods gaining prominence. However, implementing machine-learning models can be challenging due to extensive training requirements and the need for ...
Sentiment and Semantic Analysis and Urban Quality Inference using Machine Learning Algorithms
(2022-10-14)
Qualitative interviews are conducted by researchers to gain a deeper understanding
of people’s opinions and perceptions about a specific topic. The analysis of such
textual data is an iterative process and often ...
Optimizing on-chip Machine Learning for Data Prefetching
(2023-03-03)
The idea behind data prefetching is to speed up program execution by predicting
what data is needed by the processor, before it is actually needed. Data prefetching
is commonly performed by prefetching the next memory ...
Data Augmentation: Enhancing Named Entity Recognition Performance on Swedish Medical Texts
(2023-10-05)
Named Entity Recognition (NER) refers to the task of locating relevant information within text sequences. Within the medical domain, it can benefit applications such as de-identifying patient records or extracting valuable ...
Feature Selection for Microarray Data via Stochastic Approximation
(2024-03-18)
This thesis explores the challenge of feature selection (FS) in machine learning, which involves reducing the dimensionality of data. The selection of a relevant subset of features from a larger pool has demonstrated its ...