01. Kandidaatin tutkinnon opinnäytteet / Bachelor’s theses

Permanent URI for this community

https://aaltodoc.aalto.fi/handle/123456789/1

Lue lisää kandidaatin opinnäytteistä.
Read more about the Bachelor's theses.

Browse

Now showing 1 - 20 of 157

Addressing Challenges in Reinforcement Learning With Model-Based Methods
(2023-05-07) Karhula, Sandro; Spilsbury, Sam; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
Adversarial robustness of GPT-3.5 Turbo
(2024-04-26) Nguyen, Anh; Jung, Alex; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
The rapid advancement of artificial intelligence (AI) models has brought forth a critical need for a thorough examination of potential ethical and security concerns. However, many ethical issues regarding AI are being overlooked, including misinformation, bias, and accuracy. Within the scope of robustness, the study aims to assess the consistency of GPT’s output given the diversity of inputs. The primary objective is to construct a comprehensive framework for assessing the robustness of large language models (LLMs), with a specific emphasis on GPT 3.5. The research takes a proactive stance by developing experiments and methods using Python coding language, incorporating literature review and data analysis. This study answered the question how the algorithmic robustness of Large Language Models can, particularly GPT 3.5 turbo , be effectively assessed. By delving deeply into the assessment of robustness, the research seeks to address challenges associated with the widespread use of powerful language models, fostering a more secure, ethical, and transparent landscape in the field of artificial intelligence.
Analysing Customer Feedback using Sentiment Analysis for Digital Service Development
(2022-12-16) Niva, Verna; Korhonen, Ari; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
An Analysis of Boundary Conditions in Partial Differential Equations using Finite Difference Method
(2021-09-06) Lasinski, Dominik; Celik, Hasan; Insinööritieteiden korkeakoulu; St-Pierre, Luc
Analysis of dedicated techniques for source code embedding
(2022-04-15) Strozanski, Pawel; Sarsa, Sami; Perustieteiden korkeakoulu; Käpylä, Maarit
Application of Natural Language Processing in Financial News Sentiment Analysis for Stock Price Prediction
(2024-04-26) Nguyen, Hiep; Gozaliasl, Ghassem; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
This thesis studies the application of Natural Language Processing (NLP) in the analysis of financial news sentiment and its subsequent impact on stock price prediction. With the increasing complexity of the financial market, the need for advanced computational techniques to predict stock price movement is evident. This study systematically reviews current research findings on the efficacy of NLP methods in analyzing the sentiment of financial news and stock price movements. By conducting a systematic literature review on the research from the past six years, this review emphasizes the development of sentiment analysis and NLP techniques and evaluates their predictive power. A total number of 33 papers were chosen for this review. Key findings suggest that due to the recent advancement, particularly the introduction of the transformer model, the focus of NLP in stock prediction has shifted from traditional statistical-based feature representation to learning-based embedding methods. The conclusion addresses the potential of sentiment analysis as a predictive tool and suggests directions for future research. This emphasizes the need for innovative NLP applications in the financial domain to enhance investment strategies and market understanding.
The Application of Reinforcement Learning (RL) in Autonomous Ship and Collision Avoidance: A Systematic Literature Review
(2024-04-26) Rezaei, Zain; Musharraf, Mushrura; Insinööritieteiden korkeakoulu; St-Pierr , Luc
In recent years, research on the application of reinforcement learning (RL) to improve the intelligence of ships has increased significantly. This study presents a systematic literature review that examines the applications of RL in the field of autonomous ships and collision avoidance. In particular, it answers the research questions of what different RL models are used, what different inputs they process and what range of outputs they produce. In addition, the simulation environments and the choice of hyperparameters that researchers use to train these models are studied. Finally, this study highlights the current implementation challenges in this area to ensure the safe and robust use of RL models in autonomous vessels.
Applications of Causality in Reinforcement Learning
(2021-09-10) Ray, Atreya; Aushev, Alexander; Perustieteiden korkeakoulu; Käpylä, Maarit
Applying machine learning to metabolite identification: Comparing Robust Loss Functions
(2022-05-06) Pham, Binh; Huusari, Riikka; Perustieteiden korkeakoulu; Käpylä, Maarit
Artificial Intelligence and Game Theory in No-Limit Texas Hold’em Poker
(2024-04-26) Heidmets, Mark; Rintanen, Jussi; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
As with many board and card games, researchers have attempted to solve poker using both game theory and artificial intelligence (AI). However, poker is an incomplete information game that is often played with over two players, meaning that solving the game becomes more complex than for complete information heads-up (two-player) games, such as chess. For several decades, efforts to develop a superhuman AI in multiplayer poker were unsuccessful, until recently when first such AI was created. Despite that, multiplayer poker is still not completely solved, and many computational and game-theoretical challenges persist. The no-limit Texas hold’em is a game-theoretically complex and captivating version of poker, including computationally challenging aspects such as imperfect information and multiplayer dynamics. Additionally, no-limit hold’em is currently the most popular version of poker, thus receiving more focus academically as well. The thesis studies AI and game theory in no-limit Texas hold’em poker as well as the computational challenges associated with the AI models by conducting a literature review of the relevant sources in the field. The paper covers the theoretical aspects of game theory in relation to poker as well as the technological advancements in developing a competitive poker model. The algorithm used in current state-of-the-art models, counterfactual regret minimization, is discussed in more detail. Due to the computational difficul- ties with no-limit hold’em game tree, several optimizations, such as abstractions and improved versions of counterfactual regret minimization must be utilized to create a capable AI for no-limit hold’em poker.
Artificial Intelligence for Prostate Cancer Screening and Diagnosis: A State-of-the-art Review
(2024-04-26) Do, Chau; Gozaliasl, Ghassem; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit
Prostate cancer (PCa) is one of the most prevalent forms of cancer affecting men worldwide. This thesis conducts a state-of-the-art review of AI-based approaches for PCa screening and diagnosis, focusing on three major types of input data: prostate-specific antigen (PSA) screening data, magnetic resonance images (MRIs), and histopathology images. The purpose of this thesis is to determine the current extent of research into AI for PCa screening and diagnosis, highlighting recent advancements, future prospects, and remaining challenges. The selected articles show wide variability in study designs, data sources, model architectures, and evaluation approaches. Key findings suggest that even though recent research mostly focuses on AI for medical image analysis, PSA data still carries valuable information and can be combined with MRI data and other clinical variables to improve diagnostic performance. AI models inputting MRIs have demonstrated diagnostic performance surpassing the PI-RADS (prostate imaging-reporting and data system) on several coarse-level PCa classification tasks. Similarly, AI models employed for Gleason grading of histopathology images have been reported to match pathologists' performance and to improve inter-rater agreement. In addition to the literature review, an analysis consisting of 11 articles was conducted to identify the relationship between the AUC values, cohort sizes, and model types of models differentiating between clinically significant PCa (csPCa) and non-csPCa using MRIs. The analysis reveals a statistically significant positive correlation (Kendall's tau = 0.673, 95% CI: 0.348-0.944, p = 0.020) between the AUCs and the cohort sizes, suggesting that increasing cohort size might have a positive impact on performance. In addition, permutation tests with the t-test statistic indicate a statistically significant difference (t = 2.297, p = 0.032) between the mean AUC values of traditional machine learning models and deep learning models, suggesting that deep learning models might be capable of achieving better performance than traditional machine learning models on such medical image analysis tasks. Despite the great potential and significant progress of research into AI for PCa, several challenges remain, including the shortage of data, label noise, and wide variability in study implementation and evaluation. These problems call for the application of novel machine-learning techniques and collaborative research endeavors.
Assessing the Effectiveness of Biological Processes for Micropollutant Removal in Wastewater Treatment
(2023-05-19) Nyman, Jesse; Louhi-Kultanen, Marjatta; Perustieteiden korkeakoulu; Hummel , Michael
Asymptotic curves on minimal surfaces
(2022-04-15) Nagyvaradi, Balazs; Peltonen, Kirsi; Insinööritieteiden korkeakoulu; St-Pierre, Luc
Automatic Code Review with Deep Learning
(2022-04-15) Pajula, Ilari; St.Pierre, Luc; Insinööritieteiden korkeakoulu; St.Pierre, Luc
Automatic Hardware-aware Optimization of Fault-tolerant Quantum Circuits
(2024-04-26) Do, Huyen; Paler, Alexandru; Perustieteiden korkeakoulu; Raasakka, Matti
This thesis explores the calibration of quantum error correction codes (QECC) by focusing on optimizing plaquette circuits at the native gate level and assessing their error rates on real quantum hardware. The research underscores the importance of native gate optimization to boost the performance and reliability of quantum computations. A crucial part of the study involves experiments on the repetition of plaquette circuits using two ancilla qubit configurations: Type A, involving fresh ancilla qubits for each repetition, and Type B, reusing ancilla qubits. Based on the data collected, an error model was formulated. A significant result is that the error rate for a single plaquette operation using IonQ’s Aria1 Quantum Processing Unit (QPU) was 1.5%, surpassing the essential 1% threshold for effective quantum error correction, thus indicating a need for further improvements. The thesis also demonstrates the variability in QECC implementation across different quantum architectures, emphasizing the challenges in establishing universally applicable error rates. The study proposes to extend this research to evaluate the error rates of plaquette circuits on other types of quantum hardware, such as superconducting quantum computers. Future work will develop a broader understanding of QECC’s effective implementation across various quantum computing platforms, enhancing the robustness and scalability of quantum error correction methods.
Bayesian reinforcement learning: A comparative study on Bayesian model-free and model-based approaches
(2020-12-18) Lebedeff, Anna; Hegde, Pashupati; Perustieteiden korkeakoulu; Käpylä, Maarit
Big maritime data for intelligent winter navigation
(2022-04-15) Chu, Nam; Musharraf, Mashrura; Insinööritieteiden korkeakoulu; St-Pierre, Luc
Birch bark – a valuable raw material; Pre-feasibility study for producing value-added products from birch bark
(2023-09-07) Andersin, Astrid; Oinas, Pekka; Perustieteiden korkeakoulu; Hummel, Michael
Black-Box vs. White-Box Machine Learning Models in Ship Navigation — A Systematic Mapping of the Literature
(2023-05-05) Shahinas, Erald; Musharraf, Mashrura; Insinööritieteiden korkeakoulu; St-Pierre, Luc
Central Composite Design Integration for Bayesian Inference
(2023-04-28) Ananov, Georgy; Vehtari, Aki; Perustieteiden korkeakoulu; Korpi-Lagg, Maarit

Browse

Browsing 01. Kandidaatin tutkinnon opinnäytteet / Bachelor’s theses by Degree programme/Major subject "Aalto Bachelor’s Programme in Science and Technology"

Results Per Page

Sort Options