The road to intelligent asphalt concrete mixture design: A Data driven analysis of common asphalt concrete property prediction methods and a solution to the inverse problem

Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the...

Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML

More machine learning (ML) models are introduced to the field of Software Engineering (SE) and reached a stage of maturity to be considered for real-world use; But the real world is complex, and testing these models lacks often in explainability, feasibility and computational capacities. Existing research introduced meta-morphic testing to...

An Empirical Assessment on the Limits of Binary Code Summarization with Transformer-based Models

Reverse engineering binaries is required to understand and analyse programs for which the source code is unavailable. Decompilers can transform the largely unreadable binaries into a more readable source code-like representation. However, many aspects of source code, such as variable names and comments, are lost during the compilation and...

Soccer Fields as Rainfall Detectors using Machine Learning: The case of Ghana

Agriculture is an important source of income for many countries in the Global South, where it may account for as much as 25% of GDP. Precipitation is crucial for agriculture in countries like Ghana, where ~95% of farming is rainfed. Accurate rainfall observations are limited in Ghana. The sparse rain gauge network and the lack of weather radars...

Multi-Level Fairness Framework: A Socio-Technical framework for Fairness Requirements Engineering in Machine Learning

Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative...

Predicting track quality using structural elements and relative track geometry data measured by RILA: A Machine Learning Classification approach

Track quality measures differ from standard to standard. Widely used indices for track quality are the standard deviations of the rail longitudinal level data and the standard deviations of alignment data. Researchers have opted for various learning methods as to relate track quality to different parameters. Some research has gone to the point...

Recommending Log Placement Based on Code Vocabulary

Logging is a common practice of vital importance that enables developers to collect runtime information from a system. This information is then used to monitor a system's performance as it runs in production and to detect the cause of system failures. Besides its importance, logging is still a manual and difficult process. Developers rely on...

Football activity recognition: A deep learning approach to football activity recognition based on Inertial Measurement Units signals

been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...

Virtual Assistant for maintenance budget estimation: Using Machine Learning to improve the objectivity of maintenance budget estimates of civil engineering structures

With an increase of data documentation and standardization in the construction field in The Netherlands, by norms such as the NEN, there is a possibility to introduce data-driven approaches to certain areas within the construction industry. One of these is the area of budget estimation which is currently fully dependent on a cost estimating...

AI lifecycle models need to be revised: An exploratory study in Fintech

Tech-leading organizations are embracing the forthcoming artificial intelligence revolution. Intelligent systems are replacing and cooperating with traditional software components. Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems. This study aims to understand the...

Improving Test Case Generation for REST APIs Through Hierarchical Clustering

With the ever-increasing use of web APIs in modern-day applications, it is becoming more important to test the system as a whole. In the last decade, tools and approaches have been proposed to automate the creation of system-level test cases for these APIs using evolutionary algorithms (EAs). One of the limiting factors of EAs is that the...

A Monitoring System for Machine Learning Models in a Large-Scale Context

Since building a machine learning model costs a lot while following 9 stages, the automated machine learning model creation became a crucial role in a large-scale context. At the same time, a monitoring system became an essential factor for machine learning models. This thesis presents the monitoring system for machine learning models at ING in...

Late payment prediction of invoices through graph features

Keeping a steady cash flow is one of the biggest if not the biggest problem that Small to Medium Enterprises (SMEs) deal with daily. Within the different types of cash flow, Accounts Receivable (AR) classifies the balance of money that needs to be paid by the company's customers. In the most typical case, after receiving goods or services, the...

Predictive Modelling of Asphalt Concrete Functional Properties Using Multiple Linear Regression and Gradient Boosting

Despite therelevance of the road infrastructure, the mechanisms governing the mechanicalproperties of asphalt concrete pavements, are currently not sufficientlyunderstood. Many empirical models of different complexity are proposed in theliterature; however, (i) they all have below high (R2 =0.85) predictiveaccuracy; (ii)...

Artificial Intelligence in Customs Risk Management for e-Commerce: Design of a Web-crawling Architecture for the Dutch Customs Administration

The last decade saw the rise of e-commerce trade and the shift of the manufacturing industry to the emerging economies, China first of all. In this context, the European Customs Authorities experienced an explosion of small parcels coming from e-commerce websites, often from China, and faced difficulties to detect fiscal frauds and security...