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
This paper implements a ROS package for segmenting Point Clouds using Gradual Point Cloud Processing (GPCP). The implementation is integrated with RTAB-Map to produce a segmented point cloud of the environment. The RGB-D SLAM Dataset was used to validate the… Read More »Point Cloud Segmentation using Projection: A ROS Implementation
Detecting erroneous or fraudulent business transactions and corre-sponding journal entries imposes a significant challenge for auditors during annualaudits. One possible solution to cope with these problems is the use of machinelearning methods, such as an autoencoder, to identify unusual journal… Read More »XAI in the Audit Domain – Explaining an Autoencoder Model for Anomaly Detection
In the last few decades, text mining has been used to extract knowledge from free texts. Applying neural networks and deep learning to natural language processing (NLP) tasks has led to many accomplishments for real-world language problems over the years.… Read More »Language Representation Models: An Overview
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