Prediction of Aircraft Take-off Weight using Machine Learning

Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML...

Dynamic Target Time Management with Reinforcement Learning: A case study on Zurich short-haul regulated arrivals

This Master Thesis investigates the possible improvements to the Target Time Management concept to optimize the arrival flows for SWISS International Airlines. The aim is to improve operational performance based on the current model used, as well as prove that Target Time Management constitutes a valuable system to improve operations in a...

Home Energy Management System: A Machine Learning Approach

The increasing adoption of renewable energy sources, particularly photovoltaic (PV) systems in residential sectors has raised important energy balancing challenges due to the intermittent nature of energy generation. To address these challenges and prioritize cost savings for residential consumers, this research investigates the integration of...

Mitigating IoT data management security concerns through blockchain and machine learning based solutions: Study and Conceptual Design

The Internet of Things industry is expanding rapidly. However, many security breaches occur, and privacy is often at stake in traditional IoT networks. These centralized systems will not be able to cope with dynamically changing environments. In light of these risks, it is crucial to prevent and minimize the chances of attacks occurring....

Vulnerability prealerting by monitoring the online repositories of open source projects

Software security plays a crucial role in the modern world governed by software. And while closed source projects can enjoy a sense of confidentiality when addressing security issues, open source projects undertake them publicly even though just as many projects rely on them. In 50% of documented cases, the vulnerabilities could have been...

Integrating Predictive and Optimization Model for Intelligent Schedule Management based on Real Time ETA Information: A Concept of Machine Learning and Exact Solution in Petrochemical Loading Facility

This study addresses the truck rescheduling problem as the consequence of uncertain arrival time. It proposes an integrated system of predictive model powered by machine learning algorithm and exact optimization model such that it is distinct from most existing literatures in this domain. The uncertainty of truck arrival time is captured as...

Expanding the Quantified-Self: To optimize performance of knowledge workers and to minimize burnout risk

Burnouts are a severe, negative consequence of lacking performance management within firms and a result of a disbalance between energy input (e.g: sleep, social activities, physical activity) and output (e.g: workload, anxiety on the job, caring for a kid) Burnouts lead to employment challenges for firms, resulting in a potential threat for the...

Applying deep learning vs machine learning models to reproduce dry spells at point scale from satellite information in a data-scarce region: the case of northern Ghana

Large parts of the world rely on rainfed agriculture for their food security. In Africa, 90% of the agricultural yields rely only on precipitation for irrigation purposes and approximately 80% of the population’s livelihood is highly dependent on its food production. Parts of Ghana are prone to droughts and flood events due to increasing...

Global shoreline forecasting with a clustering approach

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior...

Supplier Disruption Prediction using Machine Learning in Production Environments

Recent developments in supply chains and supply chain management (SCM) lead to increased complexity and vulnerability of supply chain operations. Therefore, it is needed to better anticipate and prepare for or prevent disruptions from occurring. Increasing interest for the application of machine learning in supply chain (risk) management has...

Predicting drought-induced cracks in dikes with artifical intelligence

During intense periods of drought, the development of cracks is observed in peat and clay dikes. Asset managers of the dikes increase the inspection frequency in times of drought to be able to monitor these cracks. Significant development of the cracks contributes to the development of different failure mechanisms. In this study, the occurrence...

Probabilistic Quantification of Airspace Resilience

The resilience of the Air Traffic Management (ATM) system to disturbances is required to maintain high operation performance. Before it can be improved, the resilience of the ATM system must be quantified. The measurement of resilience requires knowledge of a system reference state. This thesis proposes a novel methodology to detect disruptions...

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...