"machine learning" - Selaus asiasanan mukaan Yrkeshögskolan Arcada
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Acid Sulfate Soils Classification and Prediction from Environmental Covariates using Extreme Learning Machines
(2023)Acid Sulfate Soil (ASS) mainly occurs because of naturally occurring phenomena. It is a sulfate-bearing sediment found in coastal areas around the globe. The highest concentration of Europe’s ASS is in Finland. It has been ... -
An evaluation of air quality during the Covid-19 pandemic
(2022)This study provides an overview of strikingly changed air quality during the present pandemic same was the motivation for this research, aims and limitations. A machine learning method to analyze the situation is proposed. ... -
Artificial Intelligence in digital marketing: now and in the future
(2021)This is a study conducted in the field of digital marketing with the usage of AI, the aim of the study was to find the impact that AI currently has on digital marketing and what it could look like in the future. The structure ... -
Car sales analysis in the Nordic Countries
(2023)Sales forecasting is an essential component of business intelligence and, artificial intelligence and predictive analytics are now essential tools for companies to predict market trends and forecast sales volumes. In ... -
Click-through Rate Prediction In Practice: A study of a click-through rate prediction system
(2019)Digital advertising is a huge business with tough competition. One of the ways to be more effective in the business is to serve better chosen ads to each user. One way to improve the ad selection is to predict the ... -
Combating Involuntary Information Leakage in Video Conferences Using Computer Vision Algorithms
(2023)Information security is an endless process. With the evolution of technologies, this topic has been broadly discussed over time. During the COVID-19 pandemic, most activities have changed from personal attendance to online, ... -
Contextualizing Environments with Semantic-Level Information: Scene recognition using computer vision and natural language processing algorithms
(2021)Indoor scene recognition and semantic information can be useful for social robots. Recently, in the field of indoor scene recognition researchers have incorporated object-level information and shown improved performances ... -
Data-Driven Modelling of Gas Turbine Engines
(2021)This study investigates and compares linear and nonlinear data-driven models of a gas turbine engine. These linear models consist of Ridge, Lasso, and Multi-Task Elastic-Net models, which are set up based on linear ... -
Dataset Imbalance treatment with re-samplers pipeline
(2022)Imbalance in dataset is an age long thing and it is receiving lots of attention because of how it impacts the outcome of models. Imbalance in the sample simply means a class of sample is over-represented while the other ... -
Edge MLOps framework for AIoT applications
(2020)Recent years witnessed a boom in IoT devices resulting in big data and demand for low latency communication giving rise to a demand for 5G Networks. This shift in the infrastructure is enabling real-time decision making ... -
Enhancing Customer feedback processing with Machine Learning in Microsoft Azure
(2022)Text Classification and Natural Language Processing (NLP) is developing fast, and all the applications are rapidly evolving, GPT-3 emerged in the field just last year and there are some open-source options in the field, ... -
Examination of air pollutant concentrations in Smart City Helsinki using data exploration and deep learning methods
(2021)Air quality has become a major concern for most of the cities around Europe due to rapid urbanization and industrialization. Smart City is an initiative to solve such problems by integrating information and communication ... -
Experimental evaluation of ship detection using U-Net with various backbone networks
(2022)The growth in global trade has led to the growth in global ship traffic. Maritime security, safety and tracking has become more critical. Organizations and governments globally are developing applications to improve maritime ... -
Explainability of time series models
(2022)The lack of interpretability of machine learning models is a drawback of their use. To better understand how the model works, how data affects its performance, how the model could be improved, and to gain trust in the ... -
Exploration of a trading strategy system based on meta-labeling and hybrid modeling using the SigTechPlatform.
(2021)The thesis aims to study a machine learning (ML) supported trading system. The methodology is based on a process that is utilizing meta-labeling, thus provides labels for a secondary model, where losses and gains are labeled ... -
Food Waste Prediction in Grocery Stores : time series forecasting by deep learning
(2022)Food waste has becoming an increasingly important problem globally. Source of waste derives from supply chain, food manufacturing, household, retail stores etc. This thesis focuses on the food waste problem in retail ... -
Fusing extreme learning machine with convolutional neural network
(Yrkeshögskolan Arcada, 2021) -
How can financial institutions more efficiently prevent credit-card fraud and AML using AI and machine learning technologies?
(2021)As society and payments are getting increasingly digitized and most of financial transactions are done by credit-card or wire-transfer, the focus on financial crime, fraud and Anti-Money Laundering is becoming a focus point ... -
Intelligent Automation in Journalism: Are newsrooms ready to let machines write our news?
(2022)The advancements in readily available structured data, artificial intelligence and automation tools have led to newsrooms exploring the possibility of creating news articles automatically. Sports, finance, politics and ... -
Large-scale Deep Learning by Distributed Training
(2019)This thesis is done as part of a service development task of distributed deep learning on the CSC provided infrastructure. The aim is to improve the readiness to provide a service for AI researchers who wish to scale out ...
