This stage seems to be most interesting one to almost all of the data scientists. Many people call it “a stage where magic happens”. But remember magic can happen only if you have correct props and technique. In terms of data science “Data” is that prop and data preparation is that technique.
Bis 2025 werden 41,6 Milliarden IoT-Geräte erwartet. Dadurch wächst die Menge an kontinuierlich generierten Daten rasant. Klassische maschinelle Lernverfahren, die auf statischen Datensätzen trainiert werden, sind nicht in der Lage, sich kontinuierlich an dynamische Umgebungen anzupassen. Problematisch ist dabei der… Read More »Adaptives maschinelles Lernen mit Mixture of Experts
Ziel dieser Arbeit ist die Entwicklung und Validierung eines automatisierten Prognosemodells für Gepäckmengen am Hamburger Flughafen unter Verwendung der Low-Code AutoML-Bibliothek PyCaret. Durch die Automatisierung signifikanter Phasen des Machine-Learning-Lebenszyklus konnten präzise Vorhersagen für Gepäckstücke pro Flug innerhalb und außerhalb der… Read More »Automatisierte prädiktive Analytik in der Gepäckabfertigung
Analyzing time series data of an electroencephalogram (EEG) is important for medicine and neuro- logical research. Due to noise and patient-specific signals it is difficult to analyze these data. Standard CNN, FNN and RNN in combination with noise filtering are… Read More »An overview of deep learning methods for EEG time series analysis
This paper presents a reinforcement learning approach for foreign exchange trading. Inspired by technical analysis methods, this approach makes use of technical indicators by encoding them into Gramian Angular Fields and searches for patterns that indicate price movements using convolutional… Read More »Scalp the Foreign Exchange Market with Deep Reinforcement Learning
Deep learning methods have gained attention in predictive process mining tasks. The most popular architectures include Recurrent Neural Networks and Convolutional Neural Networks. Inspired by the fact that Graph Neural Networks can operate directly on graph data, this work proposes… Read More »Predictive Business Process Monitoring using Graph Neural Networks
Mobile robot path planning is one of the most essential problems in robotics. This work proposes a novel PSO-based method for mobile robot local path planning in dynamic unknown environments. Using this method, the robot is able to accelerate or… Read More »Mobile Robot Path Planning in Dynamic Unknown Environments using Particle Swarm Optimization
This work introduces Multi-Fusion Network for human-object interaction detection with multiple cameras. We present a concept and implementation of the architecture for a beverage refrigerator with multiple cameras as proof-of-concept. We also introduce an effective approach for minimizing the required… Read More »Recognizing Human-Object Interaction in Multi-Camera Environments
This paper introduces a concept for a self-supervised approach to apply a pre-trained 2D model for processing 3D point clouds. This approach helps to eliminate the need for 3D labeled data, which is extremely labor-intensive to create. The approach is… Read More »Self-supervised gradual 3D point clouds processing for robots