Open thesis topics:
Topic 1: Conducting of a Causal Analysis for Sustainable Transportation Based on a Case Study
Background
Currently, we have two datasets that means there are two possible case studies to select only one of them.
The objective of the first possible case study is to investigate the variables that contribute to bike sharing becoming a practicable and environmentally friendly form of transportation. The field of causal inference and discovery is wide. Therefore, we will mainly conduct time-based causal analysis and causal discovery in this thesis work.
In the first case, examples for possible interventions like change in the ticket price, big economy change in the world, COVID, new marketing decision, big event in the city. Similarly, examples for possible cause factors (no intervention) are like the weather, the ticket price, the car petrol price, the university is close.
For another possible case study, the input data is provided by a national railway company. Data related to train traffic and the number of passengers. By supplementing the data, it is possible to study how, for example, the opening of a nearby factory changed travel habits, or what effect a new, modern train has on the traveling public. Data is also available on the trains, so e.g. delays also can be analyzed.
Main steps:
- Causal discovery algorithms used in industry (literature research)
- Algorithms for causal discovery and their implementations (literature research)
- Including the introduction and comparison of the available libraries
- (Emphasize the distinctions between causal impact analysis and causal discovery)
- Case study
- Input data preparation, visualization
- Formulating the questions we want to answer
- Library selection, implementation, verify the result
- Explain/understand the results
- One possible case study
- Data is from MOL Bubi (https://molbubi.hu/en)
- Another possible case study
- Data is from MAV-Start (https://www.mavcsoport.hu/en)
- Evaluation of the case study
During implementation, Python is the preferred language. We already looked at one of the Python versions of Google’s R package for causal invention analysis, called Causalimpact. Similarly, we have already employed the Tigramite Python package for causal discovery (i.e., while we are examining no intervention).
This work will also be a part of a PhD work, so the achieved results will be published together with it too.
References
Reading about the bike sharing in the circular economy:
Henriksson, M. and Scalzotto, J.G., 2023. Bike-sharing under pressure: The role of cycling in building circular cycling futures. Journal of Cleaner Production, 395, p.136368.
Reading about the causal discovery in public transport:
Zhang, N., Graham, D.J., Hörcher, D. and Bansal, P., 2021. A causal inference approach to measure the vulnerability of urban metro systems. Transportation, pp.1-32.
Contact
Tamas Fekete
tfekete@constructor.university
Topic 2: Unlocking Circular Economy Potential: The Role of Digital Product Passports (DPP) in Enhancing Resource Efficiency throughout Supply Chain
Background:
This thesis seeks to explore the transformative role of digital product passports (DPP) in the context of circular economy principles. In the face of escalating environmental concerns and resource scarcity, circular economy practices have emerged as a promising solution to decouple economic growth from resource depletion. Digital product passports, a concept gaining traction in various industries, present an innovative approach to enhance traceability, transparency, and sustainability across the product lifecycle. This research aims to examine the implications, challenges, and benefits of implementing digital product passports as a tool to facilitate circularity. Through an in-depth analysis of existing literature, case studies, and empirical data, this study aims to shed light on the potential of digital product passports in revolutionizing supply chain management and consumer behavior toward a more circular and sustainable economy.
Objectives (Customized)
- Investigate the foundational principles of circular economy and the current state of circular practices in industries.
- Examine the concept of digital product passports and their role in enhancing transparency and traceability throughout the product lifecycle.
- Analyze the impact of digital product passports on promoting resource efficiency, reducing waste, and fostering sustainable consumption patterns.
- Evaluate the challenges and barriers associated with the implementation of digital product passports, including technological, regulatory, and economic considerations.
- Explore case studies and best practices from industries that have successfully adopted digital product passports to promote circularity.
- Propose recommendations for the strategic integration of digital product passports into various sectors to maximize their contribution to circular economy goals.
Methodology
This research will employ a combination of systematic literature review, case studies, and empirical analysis. The literature review will provide a comprehensive understanding of circular economy principles, digital product passports, and their intersection. Case studies will be utilized to examine real-world examples of successful implementations, while empirical analysis through surveys or interviews will offer insights into stakeholder perspectives and challenges associated with adopting digital product passports.
Significance
The findings of this thesis will contribute to the growing body of knowledge on circular economy practices and the potential of digital product passports in advancing sustainability goals. The research outcomes can inform policymakers, industry professionals, and consumers about the benefits and challenges of integrating digital product passports into supply chains. Ultimately, this study aims to provide a foundation for further research and guide the strategic adoption of digital product passports as a tool for promoting resource-efficient, circular economic systems.
References
https://doi.org/10.3390/en14082289
https://www.sciencedirect.com/science/article/pii/S221282712200021X
https://www.circularise.com/blogs/digital-product-passports-dpp-what-how-and-why
Topic 3: Revolutionizing Mobility: Unveiling the Potential of Artificial Intelligence in Cooperative, Connected, and Automated Mobility (CCAM)
Background
This thesis endeavors to explore the transformative role of Artificial Intelligence (AI) in shaping the landscape of Cooperative, Connected, and Automated Mobility (CCAM). As the transportation sector undergoes a paradigm shift towards interconnected and autonomous systems, AI emerges as a critical enabler in realizing the full potential of CCAM. This research aims to delve into the multifaceted contributions of AI technologies, encompassing machine learning algorithms, data analytics, and decision-making models, in enhancing the efficiency, safety, and sustainability of cooperative and connected transportation networks. Through an extensive examination of literature, case studies, and empirical data, the study seeks to unravel the complexities and potentials of AI in the evolution of CCAM.
Objectives
- Investigate the current state of Cooperative, Connected, and Automated Mobility, identifying key challenges and opportunities in the integration of AI technologies.
- Examine the specific roles of AI in CCAM, including machine learning algorithms for intelligent decision-making, data analytics for predictive maintenance, and communication protocols for seamless connectivity.
- Assess the impact of AI-driven solutions on enhancing traffic management, safety, and overall performance within cooperative and connected transportation systems.
- Analyze the integration of AI with communication technologies such as V2X (Vehicle-to-Everything) for real-time coordination and information exchange.
- Evaluate the ethical considerations, regulatory frameworks, and societal implications surrounding the deployment of AI in CCAM.
- Explore case studies and real-world applications where AI has demonstrated significant advancements in improving efficiency and safety within CCAM.
Methodology
This research will employ a mixed-methods approach, combining a systematic literature review with case studies and empirical analysis. The literature review will synthesize existing knowledge on CCAM and the application of AI. Case studies will be used to examine real-world implementations, while empirical analysis through simulations or field studies will provide insights into the practical performance and challenges associated with AI-driven CCAM systems.
Significance
The findings of this thesis will contribute to the ongoing discourse on the convergence of AI and CCAM, providing valuable insights for researchers, policymakers, and industry stakeholders. The research outcomes aim to inform the development and deployment of AI technologies to optimize the cooperative, connected, and automated mobility landscape. Ultimately, this study strives to facilitate the creation of more intelligent, efficient, and sustainable transportation systems through the strategic integration of AI within the context of CCAM.
Reference
https://www.mdpi.com/2071-1050/13/19/10610
Topic 4: Digital Technologies Transforming Sustainable Forest Management: A Comprehensive Exploration
Background
This thesis aims to investigate and analyze the pivotal role of digital technologies in advancing sustainable forest management practices. As the world grapples with environmental challenges, there is a growing recognition of the need to balance economic development with ecological preservation. Digital technologies offer unprecedented opportunities to enhance the efficiency, precision, and sustainability of forest management processes. This research delves into the multifaceted impact of digital technologies, such as Geographic Information Systems (GIS), remote sensing, Internet of Things (IoT), and artificial intelligence, on the sustainable management of forests. Through a systematic review of existing literature, case studies, and empirical data, the study aims to contribute insights into the transformative potential of digital technologies in addressing contemporary challenges and fostering long-term ecological resilience.
Objectives
- Investigate the current state of sustainable forest management practices and identify existing challenges.
- Analyze the role of digital technologies, including GIS, remote sensing, IoT, and artificial intelligence, in addressing challenges and optimizing forest management processes.
- Explore the environmental, economic, and social implications of integrating digital technologies into sustainable forest management.
- Assess the effectiveness of existing digital tools and technologies in promoting transparency, accountability, and stakeholder engagement in forest management.
- Examine case studies from different regions to highlight successful applications of digital technologies in sustainable forest management.
- Propose recommendations and guidelines for the strategic implementation of digital technologies to enhance sustainability in forest management practices.
Methodology
This research will adopt a mixed-methods approach, combining a systematic literature review with case studies or interview. The literature review will synthesize existing knowledge on sustainable forest management and the application of digital technologies. I provide practical insights into the real-world impact of digital technologies on sustainable forest management.
Significance
The findings of this thesis will contribute to the academic discourse on the intersection of digital technologies and sustainable forest management. Additionally, the research outcomes can inform policymakers, forest managers, and technology developers on the potential benefits and challenges associated with integrating digital solutions into forestry practices. Ultimately, the study aims to provide a foundation for further research and guide the strategic adoption of digital technologies to foster sustainable and resilient forest ecosystems.
Topic 5: Conceptual model of platform for digital asset management to enable cross-company collaborative digital twin development for transport infrastructure management
Background
In the transport sector, implementing digital twins is part of the digitization measure to improve resource efficiency in infrastructure management. However, the use of digital twins is still limited due to challenges such as the lack of a shared understanding of digital twin models, complex model integration, security issues, lack of access to essential data, and high costs due to inefficient business models. A platform for asset management platform will enable cross-company collaborative development and use of digital twins (DC). The platform aims to reduce the cost and effort of creating and using digital twins that improve transparency in the planning and operation of transport infrastructures.
Research Questions
- What transport infrastructure management scenarios require digital twin solutions?
- What are the essential components of digital asset management platform for digital twin?
- Which assets and data sources are required?
Tasks
- Analysis of existing digital twin platform solutions: literature review
- Analysis of typical transport infrastructure management scenarios including actors, use-cases, digital asset and data requirements: literature review
- Identification of essential components of the platform
- Development of the conceptual model
- Validation with experts
References
BMVI (2021) Masterplan BIM Bundesfernstraßen – Digitalisierung des Planens, Bauens, Erhaltens und Betreibens im Bundesfernstraßen-bau mit der Methode Building Information Modeling (BIM) [online].
https://aws.amazon.com/de/iot-twinmaker/
https://docs.microsoft.com/en-us/azure/digital-twins/overview
https://www.bentley.com/en/products/product-line/digital-twins/itwin
https://vertexvis.com/products/vertex-digital-twin-platform
Topic 6: Analysis and development of a standardized asset model for digital twin for transport infrastructure management
Background
The introduction of DZ in companies in Germany, Austria and Switzerland is still limited. Although 54% of companies have an end-to-end strategy for DC, only 8% of companies are already fully utilizing their DC. The central challenges are understanding the data and integrating models of the digital assets (DA) of individual DCs in a simulation of the overall system. For SMEs, the questions and uncertainties lie in the business models, costs, security aspects and access to essential data/information, among other things. Therefore, having a standardized information model expressing the standard terms, taxonomy, and relations of assets is essential.
Research Questions
- What transport infrastructure management scenarios require digital twin solutions?
- Which assets and data sources are required?
- What standardized information model in transport infrastructure management exists?
- How do we express standardized terms, taxonomy, and relations the best?
Tasks
- Analysis of typical transport infrastructure management scenarios including actors, use-cases, digital asset and data requirements: literature review
- Analysis of existing standards for transport infrastructure management: literature review
- Development of asset taxonomy for digital twin for transport infrastructure management
- Formalization in OWL/RDF
- Validation
References
https://learn.microsoft.com/en-us/azure/digital-twins/concepts ontologies
https://protege.stanford.edu/publications/ontology_development/ontology101.pdf
https://www.iaarc.org/publications/fulltext/ISARC_2019_Paper_117.pdf
Topic 7: Object detection and semantic annotation from 3D images of digital twin assets for transport infrastructure management using machine learning
Background
One of the typical data formats of digital twin assets is 3D images/drawings, e.g. car, bridge, and train models. These 3D images have to be integrated into the digital twin platform by annotating and linking the 3d images to the semantic structure. Thus, objects in the 3D images have to be automatically detected based on labels/terms defined in the semantic model/ontology.
Research Questions
- What object detection method is the best to detect objects from 3D drawings?
- How can we integrate the detected object into the digital twin of transport management ontology?
Tasks
- Finding the ontology (creating a simple ontology) as the source of labelling
- Image/drawing selection and image engineering/manipulation to enrich the dataset
- Labelling
- Feature engineering
- Object detection using e.g. YOLO
- Validation
Topic 8: Development of metrics to measure the explainability of machine learning models for digital twin
Background
Extracting explainable insights from the data managed by digital twins will improve the trust of the full spectrum of stakeholders in transport infrastructure management. This can be achieved through explainable AI (XAI). However, a metric is required to assess the quality and degree of the explainability of XAI.
Research Questions
- What criteria are required to assess the explainability of machine learning?
- Are the criteria the same for all stakeholders?
- How can we calculate the score of explainability based on the identified criteria?
Tasks
- Identification of stakeholders in transport infrastructure management and their level of knowledge about AI
- Literature review on explainability measurement/assessment
- Selection of measurement/assessment criteria
- Development of calculation model
- Validation
References
https://arxiv.org/abs/1812.04608
Topic 9: Implementing Explainable DL models for sentiment analysis in the Food Delivery Services
Research Questions
How to apply Explainable Deep Learning models for Sentiment Analysis in the Food Delivery Services ?
Tasks
- Literature Review about Explainable Deep Learning and Sentiment Analysis
- Exploratory Food Delivery Data Analysis
- Model Comparison between Conventional ML/AI and Explainable AI or Interpretable ML
- Result Analysis
References
Adak, A., Pradhan, B., & Shukla, N. (2022). Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review. Foods, 11(10), 1500.
Drus, Z.; Khalid, H. Sentiment Analysis in Social Media and Its Application: Systematic Literature Review.Procedia Comput. Sci.2019,161, 707–714
Johnson, R.; Zhang, T. Effective Use of Word Order for Text Categorization with Convolutional Neural Networks.arXiv 2014,arXiv:1412.1058.
Moreno Lopez, M.; Kalita, J. Deep Learning Applied to NLP.arXiv2017,arXiv:1703.03091.
Pang, B.; Lee, L.; Vaithyanathan, S. Thumbs Up? Sentiment Classification Using Machine Learning Techniques.arXiv2002,arXiv:cs/0205070.
Contact:
Rahmat Hidayat
r.hidayat@jacobs-university.de
Topic 10: Anomaly Detection in Building Energy-Saving Information Systems Using Internet of Things and Explainable Agent-Based Algorithms
Research Questions
How to apply Explainable Agent-Based Algorithms in Building Energy Saving Information System?
Tasks
- Literature Review
- Exploratory Data Analysis
- Model Comparison
- Result Analysis
References
Forestiero, A. Metaheuristic Algorithm for Anomaly Detection in Internet of Things leveraging on a Neural-driven Multiagent System. Knowl.-Based Syst.2021,228, 107241.
Forestiero, A.; Mastroianni, C.; Spezzano, G. Reorganization and Discovery of Grid Information with Epidemic Tuning. Future Gener. Comput. Syst. 2008,24, 788–797
Forestiero, A.; Papuzzo, G. Agents-Based Algorithm for a Distributed Information System in Internet of Things.IEEE Internet Things J.2021,8,
Contact:
Rahmat Hidayat
r.hidayat@jacobs-university.de
Topic 11: Ontology and Knowledge Graph Development for Sustainability/ESG* Performance Measurement in Manufacturing
*ESG: Environment, Social, Governance
Background
The complexity of the sustainability aspects in manufacturing has grown that include factors in the environment, social, and governance (ESG). As a comprehensive sustainability report is mandatory and must meet certain minimum requirements, failure to maintain sustainability aspects can lead to potential issues, e.g. failure of risk management and incompatibility with regulations that fail business activities.
Research Questions
- How are current sustainability performances measured in the manufacturing industry?
- How to build an Ontology and Knowledge Graph (KG) to help manage ESG performance measurement and solve interoperability issues in manufacturing?
Tasks
The whole tasks are estimated will be finished in March 2023, that consists of:
- Develop ontology and KG related to ESG performance measurement.
- Utilize and analyze current sustainability performance reports and extract information using NLP and Deep learning. The candidate tool for text extraction is already available.
- Map and align extracted information to ontology and KG.
- Put data from data sources to KG.
- Visualize knowledge graph results, e.g. using Neo4j, Stardog, etc.
The work progress will manage by using a management project tool named JIRA.
References
https://www.sciencedirect.com/science/article/pii/S0167739X2033003X?via%3Dihub
Contact:
Annas Vijaya
a.vijaya@jacobs-university.de
Topic 12: Knowledge graph building for artificial intelligence for better decisions in manufacturing
Research Questions and Tasks
- What are the knowledge graphs, and how do you construct them?
- Search/introduce companies where the knowledge graphs are applied (main focus is the automotive industry)
- How can knowledge graphs and AI be used together?
- Chose a (simple) problem for a case study (earlier points can help)
- Search and compare AI algorithms/techniques focusing on the earlier introduced case study/problem
- Formulate suggestions on which algorithm/technique can use to solve the selected case study better, design the way
- This topic is based on literature research and design
- Programming is not required (kind of black box)
- This topic is feasible for Bachelor students
Contact:
Tamas Fekete
t.fekete@jacobs-university.de
Topic 13: Comparing and analysing different ontology mapping tools
Background
Ontologies are a convenient way for knowledge representation. When ontologies are involved in system integration, it requires communication between two or more ontologies. Data to-ontology Mapping is the process whereby data and ontology are semantically related at a conceptual level, i.e. correspondences are established between the data source components and the ontology components. There are some tools for different mapping formats of data into ontologies. The goal here is to compare and evaluate the existing mapping tools.
Research Questions
- What ontology mapping tools exist?
- How do these tools perform?
- What methods were these tools used?
Tasks
- Finding all the mapping tools that are exist.
- Comparing and analysing the mapping tools.
- Determining the techniques that these tools are used.
References
[1] https://link.springer.com/chapter/10.1007/978-3-319-25639-9_24.
[2] https://arxiv.org/pdf/1906.08092.pdf.
Contact:
Tina Boroukhian
t.boroukhian@jacobs-university.de
Topic 14: Causal machine learning/inference to identify the causality of factors driving people doing voluntary carbon offsetting.
Background
Voluntary carbon offsetting is an approach to compensate activities that cause intensive greenhouse gas emissions and cannot be avoided, for example, intercontinental flying, package shipping, moving trucks, etc. The compensations are done by offsetting the carbon emission to fund projects that reduce greenhouse gas emissions, for example, installation of renewable energy power plants, electrification of transportation, the introduction of efficient cookstoves, etc. Therefore, a global net-zero emission can be achieved. This thesis focuses on empirical research and should apply causal machine learning which is a novel approach in AI.
Research Questions
- What factors contribute to people who want to do voluntary carbon offsetting for their activities?
- How are the causal relationships among those factors based on collected dataset?
Tasks
- Identify potential factors in the literatures that drive the intentions of people to contribute to global net-zero emission
- Develop hypotheses and formalize causal relationship e.g. using causal graph.
- Design a survey to test the hypotheses
- Collect the data through a survey
- Causal machine learning to analyze the collected data using tools such as DoWhy, ShowWhy
- Description and discussion of the results
References
https://ideas.repec.org/a/eee/touman/v45y2014icp194-198.html
https://www.sciencedirect.com/science/article/pii/S0261517720302028
https://www.sciencedirect.com/science/article/pii/S0959652621031206 (analysis of survey data)
https://arxiv.org/abs/2206.15475 (causal machine learning)
Topic 15: Causal machine learning/inference to identify the causality of factors driving people shopping online of products having social -cultural values
Background
eCommerce customers intend to purchase products not only because of price, quality, and environment-friendliness but also because of the social-cultural values of the products. For example, a customer wants to purchase products that can contribute to the operation of an orphanage or products that are made by a rural poor community from a certain country with a particular culture. However, since the products are purchased online, it is difficult for the customers to experience the social-cultural values of the products directly. This thesis focuses on empirical research on how social-cultural values and perceptions influence purchasing behaviors of eCommerce customers. The thesis should apply causal machine learning which is a novel approach in AI.
Research Questions
- What social-cultural factors of products contribute to people purchasing online?
- How are the causal relationships among those factors based on collected dataset?
- How to measure social-cultural value of an eCommerce product?
Tasks
- Identify potential social-cultural factors on products that drive the intentions of people to purchase online through literature review
- Develop hypotheses by correlating among the identified factors. The correlations can be positive or negative
- Design a survey to test the hypotheses
- Collect the data through a survey
- Causal machine learning to analyze the collected data using tools such as DoWhy, ShowWhy
- Development of social-cultural score based on identified factors
- Description and discussion of the results
References
https://www.sciencedirect.com/science/article/pii/S0959652621012129
http://www.journals.researchub.org/index.php/JMAS/article/view/351
https://www.sciencedirect.com/science/article/pii/S0959652621031206 (analysis of survey data)
https://arxiv.org/abs/2206.15475 (causal machine learning)
Topic 16: The Role of Causal Machine Learning (CML) to Improve Decision Making in Supply Chain Management
Background
CML is an emerging and growing field in applying AI in business. Causal inference in machine learning is a statistical tool that enables AI and machine learning algorithms to reason based on cause and effect. Understanding cause and effect would make existing AI systems more intelligent and more efficient. Moreover, it can help organizations understand their data. Some possible scenarios that may apply CML [1]:
#1: In an e-commerce context, we could determine which specific factor impacts the decision to purchase a product most. We could better allocate resources to improve a particular KPI with this information. We could also rank the impact of different factors on the purchasing decision. We could determine if a given customer would have purchased a specific product if he/she had not bought other products for the last two years.
#2: In a broader sense, we could discover how and what a given business strategy could have avoided negative impacts? We could also determine how much we expect our sales to increase by implementing a specific training program to our business developers. the impact of a specific training program
#3: In the agricultural field, we often try to predict if a farmer’s crop yield will be lower this year. However, using casual inference, it will become to better understand what steps should we take to increase the harvest.
Research Questions
- What scenarios in supply chain management may apply CML?
- Which CML approach and tool is the most appropriate to improve supply chain processes?
- How to measure the most appropriate approach?
- What improvements in terms of decision making can be achieved?
Tasks
- Define causality scenarios in supply chain contexts
- Find an appropriate dataset
- Literature review on related work, different approaches of causal inference (Uplift Random Forests, Meta Learners, SEM, Bayesian Network, etc.), and tools (CausalML [3], CausalNex [4])
- Data preprocessing and exploratory analysis
- Model implementation using chosen tools
- Evaluation of the results
- Discussions and documentation
Starting Literature
[1] https://towardsdatascience.com/introduction-to-causality-in-machine-learning-4cee9467f06f
[2] Dataset examples: https://github.com/rguo12/awesome-causality-data
[3] https://causalml.readthedocs.io/en/latest/about.html
[4] https://causalnex.readthedocs.io/en/latest/01_introduction/01_introduction.html#
[5] https://innovationatwork.ieee.org/what-is-causal-inference-and-why-is-it-key-to-machine-learning/
[6] https://www.sciencedirect.com/science/article/pii/S0278431921000578
Topic 20: Ontology Alignment using Multiple Similarity Measures
Background
Ontologies are convenient way for knowledge representation. When ontologies are involved in system integration, it requires communication between two or more ontologies. Ontology alignment (a process to generate a set of correspondences between similar entities of various ontologies) can be described as follows: given two ontologies, each describing a set of discrete entities (which can be classes, properties, etc.), find the similarities that hold between these entities. For automatic ontology matching, we need to combine some kinds of different similarity measures: lexical-based, Language-based, structure-based, and semantic-based techniques as well as using information in ontologies including names, labels, comments, relations and positions of concepts in the hierarchy and integrating WordNet dictionary.
Research Questions
- Which similarity measures approaches are more suitable?
- Which features are needed to extract from the ontologies for implementing the similarities?
- Which libraries are more suitable for this task?
- Can you develop the presented similarities to reach to the better F-measure?
- Which approaches is more flexible to compute the similarities for large ontologies?
Tasks
- Studying and comparing different similarity measures approaches
- Finding and extracting suitable features from ontologies
- Implementing different similarity measures (lexical-based, Language-based, structure-based, and
semantic-based techniques) - Combining different similarity measures techniques as a final similarity
Programming Language: Python
Library: owlready2, BeautifulSoup, nltk
Starting References
[1] Nguyen, Thi Thuy Anh, and Stefan Conrad. “Ontology matching using multiple similarity measures.” 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K). Vol. 1. IEEE, 2015.
[2] Nguyen, Thi Thuy Anh, and Stefan Conrad. “Combination of lexical and structure-based similarity measures to match ontologies automatically.” International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management. Springer, Berlin, Heidelberg, 2012.
[3] Bulygin, Lev, and Sergey A. Stupnikov. “Applying of Machine Learning Techniques to Combine Stringbased, Language-based and Structure-based Similarity Measures for Ontology Matching.” DAMDID/RCDL. 2019.
Contact:
Tina Boroukhian
t.boroukhian@jacobs-university.de
Topic 21: Mapping Data (JSON, CSV) to Existing Ontologies and populate the ontologies by the Data contents using Python Libraries
Background
Ontologies are convenient way for knowledge representation. When ontologies are involved in system integration, it requires communication between two or more ontologies. Database-to-Ontology Mapping is the process whereby a database and an ontology are semantically related at a conceptual level, i.e. correspondences are established between the database components and the ontology components. For automatic mapping data to ontologies, we need to find the related components between ontology and our data and applying the mapping between them.
Research Questions
- Which ontologies are related to our data? What are the ontology domains for our data?
- Which libraries are more suitable for this task?
- Which approaches is more flexible to map the big data to ontologies?
- How can you improve the program for running the big amount of data?
Tasks
- Studying and comparing different python libraries for purpose ontology
- Finding the related components between ontology and data
- Reading JSON file from the Api and mapping it to related ontologies
- Populating the ontologies by our data contents
- Saving the mapping file into the Neo4j
Programming Language: Python
Library: owlready2, RDFLib
Starting References
[1] Munir K., Anjum M. S., (2018) The use of ontologies for effective knowledge modelling and information
retrieval, Applied Computing and Informatics , 14(2), 116-126.
[2] Shah, T., Rabhi, F. & Ray, P. (2015) Investigating an ontology-based approach for Big Data analysis of
inter-dependent medical and oral health conditions. Cluster Compute 18, 351–367 (2015). https://doi.org/
10.1007/s10586-014-0406-8.
[3] https://owlready2.readthedocs.io/en/latest/onto.html.
[4] https://neo4j.com/labs/neosemantics/4.0/introduction/
Contact:
Tina Boroukhian
t.boroukhian@jacobs-university.de
Topic 22: Ontology Alignment using Machine Learning and Neural Networks Techniques
Background
Ontologies are convenient way for knowledge representation. When ontologies are involved in system integration, it requires communication between two or more ontologies. Ontology alignment is a process of establishing correspondences between semantically related entities in different ontologies. Applying machine learning between ontology elements as features for ontology matching. Logistic Regression, Random Forest classifier and Gradient Boosting are used as machine learning methods.
Research Questions
- Which Machine Learning and neural networks Techniques are more suitable?
- Which features are needed to alignment the ontologies?
- Which libraries are more suitable for this task?
- Can you develop the presented similarities to reach to the better F-measure?
- Which approaches is more flexible to compute the alignment for large ontologies?
Tasks
- Studying and comparing different machine learning and neural network techniques
- Creating predicted alignment from two lists of entities
- Applying machine learning methods to existing similarity measures and creating dataset and training a machine learning model
Programming Language: Python
Library: scikit-learn, owlready2, BeautifulSoup
Starting References
[1] Bulygin, Lev, and Sergey A. Stupnikov. “Applying of Machine Learning Techniques to Combine Stringbased,
Language-based and Structure-based Similarity Measures for Ontology Matching.” DAMDID/RCDL.
2019.
[2] Nezhadi, Azadeh Haratian, Bita Shadgar, and Alireza Osareh. “Ontology alignment using machine
learning techniques.” International Journal of Computer Science & Information Technology 3.2 (2011): 139.
[3] Bento, Alexandre, Amal Zouaq, and Michel Gagnon. “Ontology matching using convolutional neural
networks.” Proceedings of the 12th language resources and evaluation conference. 2020.
Contact:
Atit Bashyal
a.bashyal@jacobs-university.de
Topic 23: Querying Ontologies using NLP: A Natural Language based Query Interface to OWL Ontologies for Demand-Response System
Background
Ontologies are convenient way for knowledge representation. Accessing structured data in the form of ontologies requires training and learning formal query languages (e.g., Cypher or SPARQL) which poses significant difficulties for non-expert users. One of the ways to lower the learning overhead and make ontology queries more straightforward is through a Natural Language Interface In this project we need to Implement the Question-based Interface to Ontologies – a tool for querying ontologies.
Research Questions
- How we can extracting domain knowledge automatically?
- How we can translating the text query to formal query language, e.g. Cypher or SPARQL?
- Which libraries are more suitable for this task?
Tasks
- Extracting domain knowledge automatically
- Extracting a text query as an input and transforms it to the set of queries expressed in a formal language, e.g. Cypher or SPARQL
- Connecting to Neo4j and querying ontology based on formal language, e.g. Cypher or SPARQL
Programming language and libraries
- Python
- nltk, owlready2
Starting References
[1] Damljanovic, Danica, Valentin Tablan, and Kalina Bontcheva. “A Text-based Query Interface to OWL Ontologies.” LREC. 2008.
[2] Bernstein, Abraham, et al. “Querying ontologies: A controlled english interface for end-users.” International Semantic Web Conference. Springer, Berlin, Heidelberg, 2005.
Contact:
Tina Boroukhian
t.boroukhian@jacobs-university.de
Topic 24: How artificial intelligence can improve the management of large projects? A causal machine learning approach
Background
Even though the impact of artificial intelligence, predictive analytics and machine learning has proved to be significant in different areas of automation and engineering, there is a lack of research on their effect on project management.
Research Questions
- How artificial intelligence can improve the management of large projects?
- What aspects of project management can be improved through AI?
- Which factors influence the willingness of projects/project managers to adopt AI?
- How are the causal relationships among those factors based on collected dataset?
Tasks
- Systematic and comparative literature review on the applications of AI and machine learning in project management
- Identification of aspects/factors in project management that can be improved through AI.
- Identification of factors influencing the willingness of project managers/teams to adopt AI
- Development of causal graph based on the identified factors
- Survey
- Causal machine learning to analyze the collected data using tools such as DoWhy, ShowWhy
Starting References
https://medium.com/the-project-office/artificial-intelligence-in-project-management-68ddf2ad91d7
https://www.liquidplanner.com/blog/seven-future-trends-in-project-management/
Topic 25: Managing interoperability in circular economy
Background
The circular economy concept involves collaborations of different human actors and information systems throughout the supply chain. Those different actors and systems need to share reliable data and innovative solutions with each other to improve decision makings and process efficiency. In other domains like healthcare [1], and inter-organization collaboration, ontologies and standards are used to define common understanding on a semantical level. Those ontologies and standards provide agreed vocabularies that represent entities, processes, and resources in the whole system
Research Questions
- What are typical actors, systems, and solutions involved in the circular economy?
- What vocabularies and relation models are required to facilitate the interoperability of those entities?
Tasks
- Systematic and comparative literature review
- Identification of actors, systems, and exchanged information and solutions between actors/systems
- Identification of common vocabularies to facilitate interoperability
- Development of relation model representing the vocabularies and their relations
Starting References
[1] https://www.sciencedirect.com/science/article/pii/S1877050912004024
[2] https://link.springer.com/chapter/10.1007/978-3-642-15961-9_86
[3] http://ceur-ws.org/Vol-2044/paper10/paper10.html
Topic 26: The impacts of dynamic electricity price on production planning and control
Background
The introduction of dynamic electricity prices in the manufacturing (energy consumer) requires the production planners to consider the price fluctuation in their production planning and controls. The production processes have to be shifted to the time points where the electricity price is low.
Research questions
- What activities in production planning and control will be influenced by the dynamic electricity price?
- What objectives, variables, and constraints are required to be changed compared to conventional production planning and control?
- How can production planning and control be optimized?
Tasks
- Systematic and comparative literature review
- Analysis of different dynamic price models and their impacts on the planning time horizon
- Identification objectives, variables, and constraints for the optimization
- Development of optimization model
- Solving the optimization model using tools, eg. using linear programming, ant colony optimization, etc.
Starting References
https://www.sciencedirect.com/science/article/pii/S2212827116001281
https://publikationen.bibliothek.kit.edu/1000060882
Topic 27: Energy consumption and cost prediction of customized products using data analytics or machine learning
Background
To improve the competitiveness in the market, companies transform their business by providing customized products. A stainless steel manufacturing SME, who is currently the leader in a niche market of stainless steel application in the oil and gas industry, provides customized steel products that allow customers to configure the material, size, shape, heat treatment, etc. However, it leads to challenges in estimating the price and energy consumption of the customer configured products individually.
Research Questions
- What variables influence the energy consumption and costs?
- Which data analytics/machine learning methods can be applied to estimate the energy consumption and costs? Which method is the best?
- What would be the actions to save the energy consumption and costs after predicting them?
Tasks
- Comparative literature review
- Data collection, pre-processing, exploratory data analytics
- Building machine learning models
- Evaluation of the models
- Critical analysis of results and actions required to achieve energy and cost-saving
Starting References
https://publikationen.bibliothek.kit.edu/1000060882
Topic 28: How does COVID-19 give impact to the supply chain of different products (e.g. hygiene, electronics/IT, typical product )? A data analytics approach
Background
The global COVID-19 pandemic has a strong impact to world economy due to the limitation of supply chain activities. The supply chain of certain types of products are negatively affected but not for some other types of product. This thesis focuses on the analysis of the supply chain of different types of products during the global pandemic by analyzing the available data.
Research Questions
- What types of products are strongly affected by the COVID-19 global pandemic?
- How the pandemic affects the supply chain of those products?
- What model can be used to describe the correlations of the pandemic to the supply chain. (note: focus on the different time series models e.g. Holt-Winters, ARIMA, SARIMA, VAR, etc. or regression models, e.g. linear, polynomial, random forest, ridge, etc.)
Tasks
- Comparative literature review to analyze related works
- Identification of supply chain scenarios affected by COVID-19 pandemic
- Data collection
- Exploratory data analysis
- Building the time series and regression models
- Evaluation of the results
Starting References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413852/
Topic 29: The roles of digital twins in sustainable supply chain
A digital twin can be used as a virtual supply chain replica that consists of hundreds of assets, warehouses, logistics and inventory positions [1]. It is gaining more attention in the industry due to improvements in technical and computational capabilities with operations technology.
Research Questions
- What factors make a supply chain more sustainable?
- What are the requirements for applying digital twins to a supply chain?
- What scenarios in a supply chain can be improved through digital twins?
Tasks
- Comparative literature review on sustainable supply chains and digital twins
- Identification of essential factors in applying digital twins in supply chains
- Development of general sustainable supply chain scenarios
- Two possible methodology
- Cross case analysis: combining literature studies and interview on different supply chain scenarios
- Surveys
- Analysis of literature studies + interview or survey results
Starting References
[1] https://www.supplychaindigital.com/technology/evolution-digital-twins-supply-chain
[2] https://link.springer.com/article/10.1007/s11036-020-01557-9
Student Theses
| Name | Topic | Year | Thesis Category |
|---|---|---|---|
| Edmundo Cuadra | Data Analytics Approach for Penetrating the Aircraft Manufacturing Market Duopoly | 2021 | B.Sc. IEM |
| Egor Sarafanov | Artificial Intelligence in Project Management: What factors influence the level of AI adaptation? | 2021 | B.Sc. IEM |
| Ishansh Gupta | Enhancing Football Analytics using Data Science | 2021 | M.Sc. DE |
| Korin Hoxha | Towards the Construction of a Supply Chain Management Digital Twin using Data Management and Data Analytics | 2021 | B.Sc. IEM |
| Mohid Qaiser | The effect of dynamic electricity price on production planning and control | 2021 | B.Sc. IEM |
| Oumeng Xia | How COVID-19 Give Impact to the Supply Chain of Food and Hygiene | 2021 | B.Sc. IEM |
| Pardis Sahraei | Integration of Drones Technology and the Public Transportation System for the Last Mile Delivery by Considering Dynamic Distribution Centers | 2021 | M.Sc. IEM |
| Sebastian Gonzalez | Impact of Machine Learning and Natural Language Processing on Products and their Features | 2021 | B.Sc. IEM |
| Tammy Siyakurima | How can Artificial Intelligence Improve the Management of Large Projects? | 2021 | B.Sc. IEM |
| Temirlan Aikenov | Energy consumption and cost prediction of customized products using data analytics or machine learning | 2021 | B.Sc. IEM |
| Thanh Tuan Nguyen | Handling interoperability in dynamic electricity market | 2021 | B.Sc. IEM |
| Unai Alapont | Novel Smart Contracts Model for the Infrastructure Sector: A Design Science Research Framework | 2021 | B.Sc. IEM |
| Vaibhav Apraj | A Proof of Concept for an incrementally enhanced Data Quality Dashboard | 2021 | M.Sc. DE |
| Yanny Budiaki | Reduction of food loss along the Sub-Saharan African food supply chain: The application of a sharing economy product | 2021 | B.Sc. IEM |
| Andrea Pin Morales | The Environmental Impact of Increased Transparency In Last Mile Delivery Emissions in E commerce A study on the effect of a sustainability driven point system on customer behavior | 2020 | B.Sc. IEM |
| Atit Bashyal | Moving Beyond Maximum Likelihood Estimates Application of Bayesian Inference techniques in Poverty Prediction. | 2020 | M.Sc. DE |
| Daniyar Abdimomunov | Opportunities and Challenges of Data-driven Applications Systematic comparison and validation of academic literature from the perspective of data users | 2020 | B.Sc. IEM |
| Daoyuan Ji | Collaborative Distribution Model between Logistics Service Providers and Public Transportation Operators: Investigation of parcel Movement in the Public Transit Network with Heuristics Approach | 2020 | M.Sc. SCEM |
| Helen Schmitz | How does bonus programs affect the customer behaviour towards sustainable packaging? A survey-based customer behaviour analysis | 2020 | B.Sc. IEM |
| Kataryna Taran | Current state of Facilities Management industry: Development and implementation of new standards, technologies, and strategies around the world | 2020 | M.Sc. SCEM |
| Merint Thomas Mathew | Monitoring Customer Activity and Forecasting Future Deals for improved Customer Success Management | 2020 | M.Sc. DE |
| Nathnael Taye | Energy efficiency and Digitalization: How to improve awareness on energy efficiency in residential buildings using digitalization? | 2020 | B.Sc. IEM |
| Paola Cevallos | Best Practices of Digitalization within Motorsport Events | 2020 | B.Sc. IEM |
| Paolo de Leon | How Industry 4.0 Secures the Future of the Shipbuilding Industry Data Analysis on the implementation of new IoT technologies to the Shipbuilding Industry | 2020 | B.Sc. IEM |
| Peter-Sleiman Mansour | Supplier selection model for sustainable supply chain in low-cost robotics production | 2020 | M.Sc. SCEM |
| Saurav Kedia | Carpooling in Nepal: User Prospective based on Sustainability: A survey-based approach | 2020 | B.Sc. IEM |
| Sebastian Reiser | Dynamic Electricity Pricing Comparative Study of different forecasting methods | 2020 | B.Sc. IEM |
| Surendra Ketkar | Quality Control in Smart Manufacturing for SMEs | 2020 | M.Sc. SCEM |
| Yousuf Farooq | Can Claiming Sustainability harm companies? A survey-based approach | 2020 | B.Sc. IEM |
| Zayed Balout | Industry 4.0: An Empirical and Conceptual Analysis Approach to the Implementation and Progression of Digitalization within the Construction Sector | 2020 | M.Sc. SCEM |
| Amin Houshidari | Big data analytics for forecasting in supply chain | 2019 | M.Sc. SCEM |
| Burulai Abdykapar Kyzy | The role of artificial intelligence, machine learning and predictive analytics in project management | 2019 | M.Sc. SCEM |
| Cedric Ohlms | Data and Interface Management for optimization of change processes | 2019 | B.Sc. IEM |
| Dazhi Zhan | Systematic Comparison of Impact and Readiness of Industry 4.0 Technologies between China and Germany | 2019 | B.Sc. IEM |
| Diego Lainfiesta | Capacity and Risk Planning in a high Mix Product Environment | 2019 | M.Sc. DE |
| Katharina Landers | The Use of Artificial Intelligence and Internet of Things for Anomaly Detection and Predictive Maintenance for Construction Machinery | 2019 | M.Sc. SCEM |
| Mifrah Aftab | Analyzation and Development of Logistics Chain KPIs for Sustainable Ports in Europe. | 2019 | M.Sc. SCEM |
| Milos Delikladic | How Industry 4.0 technologies can be incorporated in future farming? Survey and interview investigation in Serbia | 2019 | B.Sc. IEM |
| Miruthula Ramesh | To what extent can sustainable/green manufacturing benefit from the integration of IoT and Big data? | 2019 | B.Sc. IEM |
| Murtaza Khawaja | The Role of After Sales in an IoT based Startup | 2019 | B.Sc. IEM |
| Osama Bin Waheed | Construction 4.0 and smart building Development of the reference process model in construction 4.0 Case study: Sweden and China | 2019 | B.Sc. IEM |
| Rahul Upadhyay | Industry 4.0 in Farming: Precision Agriculture A case study in Nepal | 2019 | B.Sc. IEM |
| Sharath Abraham Peter | Application of augmented reality technology in the manufacturing process of pressing tools | 2019 | M.Sc. SCEM |
| Tianran Ni | Automated Information System Development for Medium- to Short-term Capacity Planning in MTO Industry | 2019 | B.Sc. IEM |
| Xuqi Bai | Ergonomic Performance Indicator Development for Assembly Systems: Extension of EAWS Method with Assessment of Environmental Factor | 2019 | B.Sc. IEM |
| Ali Naweed | The Role of Digitalization for transformation to Industrie 4.0 oriented Business Models in the German Automotive Industry. | 2018 | B.Sc. IEM |
| Anton Shavtvalishvili | Additive Manufacturing’s impact on supply chain in aerospace industry | 2018 | B.Sc. IEM |
| Hussein Hegazy | Outsourcing and Swap practice in the petroleum industry | 2018 | B.Sc. IEM |
| JongHack Yi | Development of Sales Demand Forecast through process Improvement | 2018 | M.Sc. SCEM |
| Kemal Tumysch | Outsourcing and Enterprise Resource Planning: Challenges and Opportunity in Petroleum Supply Chain | 2018 | B.Sc. IEM |
| Kirsten Mueller | Analysis of NBA supply chain network for its expansion to Europe | 2018 | M.Sc. SCEM |
| Osaid Khan | Assessing the Maturity Level of Industry 4.0 in the Automotive Industry | 2018 | B.Sc. IEM |
| Sergi Drago Gonzalez | Do machine learning models provide accurate macroeconomic GDP forecast? | 2018 | B.Sc. GEM |
| Soman Tariq | Reinventing the Manufacturing Sector of Aerospace Industry by Implementing Industry 4.0 Technologies | 2018 | B.Sc. IEM |
| Waleed Asif | Improving Energy and Resource Efficiency in Different Level of Production Using Industry 4.0 Technologies | 2018 | B.Sc. IEM |
| Young Joon Lee | Product Lifecycle Information Model for Reference Architectural Model Industrie 4.0 (RAMI 4.0) | 2018 | M.Sc. SCEM |
| Akarawint Chawalitanont | Power Consumption Prediction of Customised Stainless Steel Ring Products using Hierarchical Bayesian Models and Artificial Neural Networks | 2022 | M.Sc. DE |
| Nadhir Khoubeieb Mechai | Causal Machine Learning (CML) for Demand Forecasting of Semiconductor Products | 2022 | M.Sc. SCM |
| Xiaotong Fan | Advanced Time Series Analysis for Electric Vehicle Demand Forecasting | 2022 | M.Sc. SCM |
| Seyed Taha Raeisi | Delivery Assurance and Digitalization Utilizing Expert’s Knowledge, Internal Key Performance Indexes and Exogenous Risk Factors to Drive Operational Supply Chain | 2022 | M.Sc. SCM |
| Vamsi Sai Pidikiti | Managing interoperability in the circular economy | 2022 | M.Sc. SCM |
| Muhammad Abdullah Shah | Using Machine Learning to Optimize Dynamic Pricing in Production at Machine Level | 2022 | B.Sc. IEM |
| Nurgun Rafizade, | Exploration of Radial Basis Function Networks and Adaptive Network Based Fuzzy Inference Systems for Time Series Forecasting | 2022 | B.Sc. CS |
| Hamza Hayak | Explainable AI for Crop Yield Prediction | 2022 | B.Sc. CS |
| Toska Ymerhalili | Deep Reinforcement Learning for Industrial Microgrid Management | 2022 | B.Sc. CS |
| Nguyen Duy Bao | Ensemble Learning to forecast Wind/Solar power generation | 2022 | B.Sc. IEM |
| Carlotta Buck | Incentivising Voluntary Carbon Offsetting and its Driving Factors: Integrating Causal and Statistical Data Analysis | 2022 | B.Sc. IEM |
| Abel Teklearegay | Ontology Matching Using Supervised Learning Techniques | 2022 | B.Sc. IEM |
| Christopher Mclaughlan | Querying Ontologies using NLP: A Natural Language based Query Interface to OWL Ontologies for Demand-Response System | 2022 | B.Sc. CS |
| Kaleb Kristo | What social-cultural values of products make people shop online? A causal data analytics method | 2022 | B.Sc. IEM |
| Moonkyoung Choo | Effect of Governmental Policies and Healthcare Systems on COVID-19 Hosptalization: A Machine Learning Prediction Approach | 2022 | B.Sc. IEM |
| Karen Okoli | The Role of AI in Circular Economy: Automotive industry | 2022 | B.Sc. IEM |
| Kezia Adesanya | Mapping JSON data to Existing Ontologies: Populating the ontologies by the Data contents using Python Libraries | 2022 | B.Sc. IEM |
| Mariam Bukhrashvili | The impact of Covid-19 on Quality Audits The Future of pharmaceutical quality audits | 2022 | B.Sc. IEM |
| Mohamed Ghandour | Thesis 2022 21. Mohamed Ghandour, Product Portfolio Complexity Management a data-driven approach using ‘Analytical Hierarchy Process’ and the ‘Technique for Order Performance by Similarity to Ideal Solution | 2022 | B.Sc. IEM |
| Mulisa Kevine Umwiza | The Role of Digital Twin in Sustainable Supply Chain in the Automotive Industry | 2022 | B.Sc. IEM |
| Rauan Beibit | COVID-19 Impact on the Supply Chain Effects of the Semiconductor Shortage on the Automotive Industry | 2022 | B.Sc. IEM |
| Stefan Krstevski | The Influence of Dynamic Electricity Price on Production Scheduling: A Mixed Integer Linear Programming Model | 2022 | B.Sc. IEM |
| Ylva Reinhold | How Do Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of IoT in Novel Sectors | 2022 | B.Sc. IEM |
| Xuchong Du | Application of GNN in supply chain for link prediction and explainability analysis | 2022 | B.Sc. CS |
| Apivut Ourairat | Gap Analysis, State of the Art, Existing Artificial Intelligence in Agrifood supply chain | 2022 | B.Sc. IEM |
| Abumansur Sabyrrakhim | Mapping SQL (Pre)-clinical Data from Cancer Radiooncology and Radiobiology Studies to Ontology | 2022 | B.Sc. CS |
| Dhushyanth Ravindran | Systematic Literature Review and Conceptual Model - Artificial Intelligence in Agri-based Food Supply Chain | 2023 | M.Sc. SCM |
| Mehr Un Nisa | Conceptual model of platform for digital asset management to enable cross-company collaborative digital twin development for transport infrastructure management | 2023 | M.Sc. SCM |
| Vikas Kumar | Logistic KPI Analysis for Wind Turbine Industries | 2023 | M.Sc. SCM |
| Adriana Martinez | Decision-Targeted Operational Supply Chain: A Comparative Study of Machine Learning Approaches for Supplier Escalation in the Operational Supply Chain Using Multi-Criteria Decision-Making (MCDM) | 2023 | M.Sc. DSSB |
| Siyakurima Tammy Tacheneswa | Identifying Essential Driving Factors of Digital Transformation Maturity Models Using Fuzzy MCDM Methods: A Company Based Survey Approach | 2023 | M.Sc. DSSB |
| Aruzhan Nurzhumina | Towards Ontology based Model for Circular E conomy S cenarios in Automotive Industry A Systematic Literature Review Approach | 2023 | B.Sc. IEM |
| Assiya Zakay | Cross Company Digital Twin Development in Circular Economy: Conceptual Model for a Digital Asset Management Platform | 2023 | B.Sc. IEM |
| Heinn Thant Zaw | Predicting Stock Prices and Trend Using Machine Learning Data analytics approach using Random Forrest and Long Short Term Memory | 2023 | B.Sc. IEM |
| Jiyuan Wei | Why people are willing to work from home in post-COVID-19 era? A survey and causal analysis approach | 2023 | B.Sc. IEM |
| John Atanacio | Explainable Artificial Intelligence in Crop Yield Prediction Analyzing the impact of climate change in German crops | 2023 | B.Sc. IEM |