2nd INTERNATIONAL DATA ANALYTICS AND MACHINE LEARNING CONFERENCE DATAMACLEA’25 ABSTRACT PROCEEDINGS BOOK
Ankara Yıldırım Beyazıt Üniversitesi, Ankara, 2025
- Yayın Türü: Kitap / Bildiri Kitabı
- Basım Tarihi: 2025
- Yayınevi: Ankara Yıldırım Beyazıt Üniversitesi
- Basıldığı Şehir: Ankara
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
2nd International Data Analytics and Machine Learning Conference (DATAMACLEA’25)
Conference Overview
The Econometric Research Association (ERA) successfully organized the 2nd International Data Analytics and Machine Learning Conference (DATAMACLEA’25) on May 5–6, 2025. The event was held both face-to-face at Ankara Yıldırım Beyazıt University, Etlik Milli İrade Campus and online, ensuring broad international participation.
This year’s main theme was:
“The Economics of Data: Foundations for Policy and Regulation.”
The conference emphasized how data has become central to policymakers and regulators, serving as the “fuel” of the digital economy while also emerging as a key geopolitical issue. Despite the growing importance of data, the underlying “economics of data” remains underexplored. The conference aimed to foster a deeper understanding of data’s economic features, such as its nature as an economic good and the value chain associated with it. The discussions stressed the need for sound economic reasoning to guide data policy, regulation, and the design of data spaces.
The event was organized in cooperation with Ankara Yıldırım Beyazıt University, Institute for International Relations and Strategic Research (ULISA- IIRSR).
Topics Covered
Participants presented original research papers and case studies aligned with, but not limited to, the following areas:
1. Data Economy and Policies
• Data policy and regulation of data spaces
• Data and the digital economy
• Economics of data
• The nature of data as an economic good
• Value chains associated with data
2. Machine Learning and Data Mining
• Machine learning models and algorithms (unsupervised learning, deep learning, reinforcement learning, ensemble methods)
• Data mining and big data analytics (feature engineering, dimensionality reduction, anomaly detection, text mining, web scraping)
• Scientific machine learning approaches
• Simulation and machine learning interactions
• Applications of machine learning approaches
3. Predictive Analytics and Forecasting
• Predictive analytics and modeling (demand forecasting, risk analysis, market dynamics, econometric modeling)
• Time series analysis and forecasting (econometric forecasting, signal processing, trend analysis, anomaly detection)
4. Natural Language Processing and Financial Data Analytics
• Natural language processing for economic insights (sentiment analysis, opinion mining, financial news analysis, chatbots)
5. Statistical Learning and Inference
• Bayesian methods, causal inference, machine learning interpretability, and statistical model selection